Sunspots and Sea Surface Temperature

I thought I was done with sunspots … but as the well-known climate scientist Michael Corleone once remarked, “Just when I thought I was out … they pull me back in”. In this case Marcel Crok, the well-known Dutch climate writer, asked me if I’d seen the paper from Nir Shaviv called “Using the Oceans as a Calorimeter to Quantify the Solar Radiative Forcing”, available here. Dr. Shaviv’s paper claims that both the ocean heat content and the ocean sea surface temperature (SST) vary in step with the ~11 year solar cycle. Although it’s not clear what “we” means when he uses it, he says: “We find that the total radiative forcing associated with solar cycles variations is about 5 to 7 times larger than just those associated with the TSI variations, thus implying the necessary existence of an amplification mechanism, though without pointing to which one.” Since the ocean heat content data is both spotty and incomplete, I looked to see if the much more extensive SST data actually showed signs of the claimed solar-related variation.

To start with, here’s what Shaviv2008 says about the treatment of the data:

Before deriving the global heat flux from the observed ocean heat content, it is worth while to study in more detail the different data sets we used, and in particular, to better understand their limitations. Since we wish to compare them to each other, we begin by creating comparable data sets, with the same resolution and time range. Thus, we down sample higher resolution data into one year bins and truncate all data sets to the range of 1955 to 2003.

I assume the 1955 start of their data is because the ocean heat content data starts in 1955. Their study uses the HadISST dataset, the “Ice and Sea Surface Temperature” data, so I went to the marvelous KNMI site and got that data to compare to the sunspot data. Here are the untruncated versions of the SIDC sunspot and the HadISST sea surface temperature data.

So … is there a solar component to the SST data? Well, looking at Figure 1, for starters we can say that if there is a solar component to SST, it’s pretty small. How small? Well, for that we need the math. I often start with a cross-correlation. A cross-correlation looks not only at how well correlated two datasets might be. It also shows how well correlated the two datasets are with a lag between the two. Figure 2 shows the cross-correlation between the sunspots and the SST:

Figure 2. Cross-correlation, sunspots and sea surface temperatures. Note that they are not significant at any lag, and that’s without accounting for autocorrelation.

So … I’m not seeing anything significant in the cross-correlation over full overlap of the two datasets, which is the period 1870-2013. However, they haven’t used the full dataset, only the part from 1955 to 2003. That’s only 49 years … and right then I start getting nervous. Remember, we’re looking for an 11-year cycle. So results from that particular half-century of data only represent three complete solar cycles, and that’s skinny … but in any case, here’s cross-correlation on the truncated datasets 1955-2003:

Figure 3. Cross-correlation, truncated sunspots and sea surface temperatures 1955-2003. Note that while they are larger than for the full dataset, they are still not significant at any lag, and that’s without accounting for autocorrelation.

Well, I can see how if all you looked at was the shortened datasets you might believe that there is a correlation between SST and sunspots. Figure 3 at least shows a positive correlation with no lag, one which is almost statistically significant if you ignore autocorrelation.

But remember, in the cross-correlation of the complete dataset shown back in Figure 2, the no-lag correlation is … well … zero. The apparent correlation shown in the half-century dataset disappears entirely when we look at the full 140-year dataset.

This highlights a huge recurring problem with analyzing natural datasets and looking for regular cycles. Regular cycles which are apparently real appear, last for a half century or even a century, and then disappear for a century …

Now, in Shaviv2008, the author suggests a way around this conundrum, viz:

Another way of visualizing the results, is to fold the data over the 11-year solar cycle and average. This reduces the relative contribution of sources uncorrelated with the solar activity as they will tend to average out (whether they are real or noise).

In support of this claim, he shows the following figure:

Figure 4. This shows Figure 5 from the Shaviv2008 paper. Of interest to this post is the top panel, showing the ostensible variation in the averaged cycles.

Now, I’ve used this technique myself. However, if I were to do it, I wouldn’t do it the way he has. He has aligned the solar minimum at time t=0, and then averaged the data for the 11 years after that. If I were doing it, I think I’d align them at the peak, and then take the averages for say six years on either side of the peak.

But in any case, rather than do it my way, I figured I’d see if I could emulate his results. Unfortunately, I ran into some issues right away when I started to do the actual calculations. Here’s the first issue:

Figure 5. The data used in Shaviv2008 to show the putative sunspot-SST relationship.

I’m sure you can see the problem. Because the dataset is so short (n = 49 years), there are only four solar minima—1964, 1976, 1986, and 1996. And since the truncated data ends in 2003, that means that we only have three complete solar cycles during the period.

This leads directly to a second problem, which is the size of the uncertainty of the results of the “folded” data. With only three full cycles to analyze, the uncertainty gets quite large. Here are the three folded datasets, along with the mean and the 95% confidence interval on the mean.

Figure 6. Sea surface temperatures from three full solar cycles, “folded” over the 11-year solar cycle as described in Shaviv2008

Now, when I’m looking for a repetitive cycle, I look at the 95% confidence interval of the mean. If the 95%CI includes the zero line, it means the variation is not significant. The problem in Figure 6, of course, is the fact that there are only three cycles in the dataset. As a result, the 95%CI goes “from the floor to the ceiling”, as the saying goes, and the results are not significant in the slightest.

So why does the Shaviv2008 result shown in Figure 4 look so convincing? Well … it’s because he’s only showing one standard error as the uncertainty in his results, when what is relevant is the 95%CI. If he showed the 95%CI, it would be obvious that the results are not significant.

However, none of that matters. Why not? Well, because the claimed effect disappears when we use the full SST and sunspot datasets. Their common period goes from 1870 through 2013, so there are many more cycles to average. Figure 7 shows the same type of “folded” analysis, except this time for the full period 1870-2013:

Figure 7. Sea surface temperatures from all solar cycles from 1870-2013, “folded” over the 11-year solar cycle as described in Shaviv2008

Here, we see the same thing that was revealed by the cross-correlation. The apparent cycle that seemed to be present in the most recent half-century of the data, the apparent cycle that is shown in Shaviv2008, that cycle disappears entirely when we look at the full dataset. And despite having a much narrower 95%CI because we have more data, once again there is no statistically significant departure from zero. At no time do we see anything unexplainable or unusual at all

And so once again, I find that the claims of a connection between the sun and climate evaporate when they are examined closely.

Let me be clear about what I am saying and not saying here. I am NOT saying that the sun doesn’t affect the climate.

What I am saying is that I still haven’t found any convincing sign of the ~11-year sunspot cycle in any climate dataset, nor has anyone pointed out such a dataset. And without that, it’s very hard to believe that even smaller secular variations in solar strength can have a significant effect on the climate.

So, for what I hope will be the final time, let me put out the challenge once again. Where is the climate dataset that shows the ~11-year sunspot/magnetism/cosmic rays/solar wind cycle? Shaviv echoes many others when he claims that there is some unknown amplification mechanism that makes the effects “about 5 to 7 times larger than just those associated with the TSI variations” … however, I’m not seeing it. So where can we find this mystery ~11-year cycle?

My Usual Request: If you disagree with someone, myself included, please QUOTE THE EXACT WORDS YOU DISAGREE WITH. This prevents many flavors of misunderstanding, and lets us all see just what it is that you think is incorrect.

Subject: This post is about the quest for the 11-year solar cycle. It is not about your pet theory about 19.8 year Jupiter/Saturn synoptic cycles. If you wish to write about them, this is not the place. Take it to Tallbloke’s Talkshop, they enjoy discussing those kinds of cycles. Here, I’m looking for the 11-year sunspot cycles in weather data, so let me ask you kindly to restrict your comments to subjects involving those cycles.

Data and Code: I’ve put the sunspot and HadISST annual data online, along with the R computer code, in a single zipped folder called “Shaviv Folder.zip“

Where is the climate dataset that shows the ~11-year sunspot/magnetism/cosmic rays/solar wind cycle? Shaviv echoes many others when he claims that there is some unknown amplification mechanism that makes the effects “about 5 to 7 times larger than just those associated with the TSI variations” … however, I’m not seeing it. So where can we find this mystery ~11-year cycle?

henry says

it is very simple really. you must just look at the right parameters. As stated before, I would not look at SSN for various reasons. I am not going to argue that again.
Here are my latest results for the drop in maximum temperatures
change/decrease in maximum temperatures (henry’s global average, 27 stations NH and 27 station SH)
last 40 years (from 1974) +0.034 degree C/yr
last 34 years (from 1980) +0.026 degree C/ yr
last 24 years (from 1990) +0.014 degree C/yr

Here is a graph showing the drop in the magnetic fields from the sun

clearly you can draw a binomial from top and bottom, as a best fit for the general drop in field strength, coming to a lowest point soon? Clearly you can see a binomial for the drop in maximum temperatures?
There must be a correlation between the drop in energy coming in (maxima) and drop in field strength.
(my proposed mechanism) The mechanism is that lower field strengths on the sun allow somewhat more of the most energetic particles to escape from the sun, hence the noted increase in ozone and others TOA. In turn, more ozone and others deflect more sunlight to outer space due to absorbance and re-radiation. Hence we are cooling, globally.

Looking for evidence of solar influence on climate in 11 year cycles is a dead end. Solar variation can only have caused about 0.8C in 150 years. Claiming that because 11 year cycles cannot be seen in noisy incomplete data so solar variation has little influence is also a dead end.

The mechanism for solar influence is simple. The UV frequencies that vary most greatly between cycles are the frequencies that penetrate below the diurnal overturning layer of the oceans, allowing energy to accumulate. This process is slow and cumulative. The apparent absence of clear 11 year cycles in ocean temps in no way shape or form disproves solar variation driving climate changes observed over 150 years.

In contrast, AGW due to CO2 is easily disproved. Empirical experiment shows the oceans are not warmed by DWLWIR. Empirical experiment also shows that the oceans respond as a selective surface to incoming solar radiation not as a “near blackbody” as per the crazed claims of climastrologists. Further, the effective emissivity (not the apparent emissivity) of water is below 0.8. The oceans need the atmosphere to cool and the atmosphere in turn needs radiative gases to cool. Global warming due to CO2 is physically impossible.

That only leaves two options for 0.8C in 150 years. Internal variability or solar variability. Solar variability is prime suspect. All you need to understand is that water is not a “near blackbody” or even close.

Shouldn’t the sunspots (or whatever the real solar driver is) drive the FIRST DERIVATIVE of temperature, not simply the temperature? If so, then the earth’s temperature might have a slow impulse response which could be much longer than 11 years. The result could easily act like a low-pass filter — and you wouldn’t see much happening within the 11 year periodicity of the sunspot cycles. BUT, give us have a few weak cycles in a row and the accumulated effect would be significant.

Given the radiative heat loss that results from higher temperatures, a good model (of global temp as a function of sunspots) might be some kind of exponential smoothing. That would be true even if global temp responded quickly enough to “follow” the 11 year solar cycle, but I’m thinking that the right smoothing constant would be too small for that.

Another way to think of this is as a low-pass RC circuit. The input is a current source which varies over time (but is not AC; it’s always >0). The circuit is a capacitor to ground (representing the thermal inertial of the earth) and a resistor to ground (representing the radiative loss as a function of temperature.) Over a small temperature range, we can model the radiative loss as proportional to temperature and ignore the higher order effects.

Either way, it’d be interesting to see if you can get a better fit, showing temperature rising after strong solar cycles and falling after weaker ones. Unfortunately, the El Nino/La Nina cycles add a hell of a lot of noise to the data.

So, let’s play the ‘maybe sunspots-co-relate-to-something-else-in-the-sun-that-might-affect-global temperatures on earth over long periods of time game. It does make sense somehow, but we may not (actually really and absolutely do not know what) that relationship might be.

Look again above at the plots.

Add a lag.
Look not a 11 year cycle, but a six-sunspot 11 year (33 positive/33 year negative) 66 year cycle of alternating “positive” and “negative” cycles that themselves are near-equal, but over a three set cycle may mean something important .

These studies by Willis aimed at reproducing important (apparently) research findings are of enormous scientific value. The issue of research repeatability has been highlighted recently in this Nature editorial concerning preclinical cancer research:

In short, the pharmaceutical company Amgen in California tried to repeat 53 “landmark” cancer genetic studies and was able to do so in only 6 cases. The German company Bayer tried the same thing with a different (partly overlapping) set of studies and could reproduce only 25% of them.

This shocking result is prompting a change in research and publication practice with more encouragement of attemps to repeat published research. While not as glamourous as blazing a trail with an (apparently) original finding, it does a service to science of great value.

Hale cycle (the “plus” + the “minus” ) is from 1970-1990 and the next is from 1990-2013.
However, fleld strengths are so low that it must come to a dead end stop, probably reversing. My bet is on 2015 or 2016. For the next 44 years (from 2015 or 2016) field strengths will be the mirror of previous 44 years, unless somebody knows better?

We hold similar views, and recall that we engaged in much discussion on this on Willis’ article on ‘Radiating the Oceans’ (which i personally consider to be not one of Willis’ stronger articles – sorry willis, just my personal opinion). However, the point that the warmists would raise is that if DWLWIR heats the atmosphere, even if it does not heat the ocean below, then due to the warmer atmosphere above the ocean,the heat loss from the ocean is lower/slower, thereby helping to maintain or even produce higher ocean temperatures over time.

In another Willis’ article (I can’t rember which one), I pointed out that if one considers the optical absorption characteristics of LWIR in water, if DWLWIR is of the level claimed by K&T (in their energy budget cartoon), and if that energy is absorbed by the oceans in accordance with accepted LWIR absorption charactericis in water, there would be so much energy absorbed in the first few micron layer that this potentially would produce about 14 to 18 metres of rainful annually. I suggested that since we are not observing such levels of annual rainfall, it suggests that DWLWIR is not of the level claimed, or it lacks sensible energy, or it is simply not (for some other reason) being absorbed by the oceans.

Indeed, I am sceptical as to whether all the DWLWIR can even reach the oceans. The average wind conditions over the oceans s BF 4 to 5. in these wind conditions, there is already quite a lot of wind swept spray and spume. Spray and spume is, of course, a fine mist of water droplets, and these droplets are more than a few microns in size and therefore would be capable of absorbing about 60% of all DWLWIR before it even reaches the ocean below.

Now I am not suggesting that there is a homogenous layer of windswept spray and spume which universally covers all the oceans all of the time. But what one has to rememeber is that globally, as you read this there are large areas of the oceans which are subject to BF 7 to 8 and above. Indeed, there will be areas of the ocean where large storms are raging with BF10 conditions. We have all seen the size of huricanes and cyclones, and one can imagine the area and sea state involved. Where these storms are raging, the spray and spume which is a layer of water droplets completely divorced from the ocean below, acts much like sun cream (or a sun parasol), but blocking DWLWIR from impacting the ocean below. So it would appear that a not insignificant proportion of the DWLWIR in the K&T energy budget cartoon, does not, or cannot reach the ocean below.

The divorced layer of spray and spume in these conditions would almost fully absorb DWLWIR and as it does so, it would heat and would be carried upwards in the atmosphere initally warming the atmosphere and keeping the DWLWIR away from the ocean below.

I have yet to see a wholly convincing argument as to why if DWLWIR is of the order suggested by K&T (in their energy budget cartoon), and if DWLWIR is absorpbed by the oceans in accordance with accepted LWIR absorption charateristics in water, there is not a very substantial amount of rainfall (much more than we observe annually) and/or that this would not lead to copious amounts of evaporation.

When considering this, one has to bear in mind that the heat flux is upwards in the first few millimetres of the ocean such that energy absorbed in the first few microns cannot find its way downwards by conduction, and ocean over turning is a slow mechanical process such that even if the top few microns of the ocean are over turned, the rate of overturning would be slower than the speed at which energy is absorption in the first few micron layer 9such that the ocean over turning process does not disipate energy downwards at a quick enough rate to stop the rapid evaporation that one would expect to see at the top of the ocean give the amount and rate of absorption of DWLWIR in the first 3 or 4 microns of the ocean).

Hale cycle (the “plus” + the “minus” ) is from 1970-1990 and the next is from 1990-2013.

Oh, I can – And will!! – agree with you about the coming slower sunspot/solar cycle-magnetic field total questions.

But!!!! I do NOT know what will happen due to that change.
So, to cover for that lack of knowledge, I would prefer to focus on the earlier longer-term 66 year patterns of “several high, several low” cycles we see since 1650 as the earth warms from the LIA. Do those cycles matter?

A problem with using a long time series of SST is that early measures are not comparable to more recent ones. There has been a long debate about adjustments for different buckets used to measure sea water temperatures, buckets versus engine intake, and the changing coverage of the oceans because temperatures were only measured where ships went in the pre-satellite/ARGO days. I seem to recall that both John Daly and Steve McIntyre have discussed these measurement issues.

Henry says
according to AGW theory (warming caused by more CO2) minimum temperatures should show a rise. Namely, it is alleged that increased GHG causes a delay in cooling.
Consequently, minimum temps. should be rising.
Here are my latest results for the change in the speed of minimum temperatures (27 weather stations NH + 27 weather stations NH, balanced to zero latitude and 70/30 @sea/inland)
last 40 years (from 1974) +0.004 degree C/yr
last 34 years (from 1980) +0.007 degree C/ yr
last 24 years (from 1990) +0.004 degree C/yr
last 14 years (from 2000) -0.009 degree/yr

Now, note that the observed values are very low, indeed, yet it seems they are significant.
Namely, setting the periods out against the speed of warming/cooling I get a binomial again with rsquared eual to 1 (100% correlation)
There is no error in the equation…..
Hence, there is no AGW There is no room for it in my equation.
unless AGW behaves naturally?
ergo
Temperature depends on solar variability only.
Have a good weekend.

Another article I have read was that the SST in the (sub)tropics rapidely increases with 0.3-0.5 K and back over a cycle, but I lost the link.

I am agnostic on that point (solar cycle influence on SST), but there is definitely a response of weather patterns on the solar cycle via the UV-route: UV increases about 10% during solar maxima, which alters O3 abundance in the tropical lower stratosphere, increases its temperature (1 K), increases the temperature difference with the poles at that height and shifts the jetstreams polewards, including the connected wind-, cloud- and rainpatterns. That is reflected in river flows. Several findings show the correlations:

At the crux of the point raised by Willis is that all the various data sets that are used in climate science are not fit for purpose. Unfortunately, this is not sufficiently recognised and/or accepted by the scientist who seek to use those data sets.

Unfortunately, they are all either of too short a duration, and/or have too wide an error margin and/or not enough sample saturation and/or are horribly bastardised by dropouts, pollution by UHI and/or endless bastardisation caused by adjustments made to the data set, the need for and correctness of which is moot.

This is one reason why one cannot see any first order correlation between the rise of CO2 levels and temperature in any of the instrument data sets. The signal from CO2 (if indeed there is any signal) is too small to be revealed in the noisy data sets that we have available, especially given the margin of error in the measurements undertaken.

If there is indeed manmade global warming, the only two data sets of note are the CO2 data set and ocean temperture data sets (ocean temperatures are a metric for energy absorption and hence imbalance, and the heat capacicty and energy stored in the oceans overwhelms that of the atmosphere by orders of magnitude).

Unfortunately, pre ARGO, there is no reliable ocean temperature data, and even post ARGO there are problems; first the adjustment made to ARGO data when it came on stream which data suggested that the oceans were cooling, second, the coverage – the oceans are vast and there are few ARGO buoys given the area and volume involved, and third, it is not known whether there is an in built bias caused by the free floating nature of the buoys which may have a tenedancy to be influenced by ocean currents which currents are themselves an artifact of variations in ocean temperature and/or density).

I am not surprised by Willis’ evaluation. It is what one would expect given the inadequacies of the available data sets, and the tendancy for scientists to over stretch the limits of the data available. Further the very nature of a coupled non linear chaotic system makes identification of trends and signals and corresponding responses extremely difficult to detect.

Let me just get this straight, you’re talking about the ‘year’ 1870 and you’re saying that somebody measured the ‘global’ sea surface temperature to within 1/10 of a degree C back then and came up with the 0.2C anomaly?

The presence of a longer term periodic change would also explain why the 11y “folding” (which is a pretty inappropriate term since it implies some kind of reversal) ends up with just noise. If the long period is near 22y using that as the offset may be more appropriate.

Indeed. The primary effect of a radiative forcing is dT/dt as can be seen by the physical dimensions. Radiation flux is power ; temp is energy. They are orthogonal, so the initial investigation should be rad (SSN) vs temp.

If there is an effect it shoud accumulate but integrating with the huge capacity of the oceans will great smooth out any signal to the point where Willis’ one-size-fits-all 0.2 significance test will fail.

There is auto-correlation in temperature for precisely this reason. Willis makes reference to it on two occassions but seems imply that the results will be even worse if he accounted for autocorrelation, without saying why.

The most current way to remove autocorrelation is by taking the first different. This would in fact be dT/dt !

Just a short point which is applicable to nearly all considerations regarding the effects of Solar irradiance, one cannot properly consider this in the absence of reliable data on cloud cover.

Until we know the extent of cloudiness over time (area, volume, composition, height of cloud stack, time of formation, duration of formation, place of formation, the underlying albedo which is being shielded by cloud cover, the surface type below the cloud cover and its absorption characteristics etc), one cannot reasonably consider how much energy is being imparted to the surface and without that one cannot begin to consider what sort of response one is expecting to see.

“That only leaves two options for 0.8C in 150 years. Internal variability or solar variability. Solar variability is prime suspect. All you need to understand is that water is not a “near blackbody” or even close.”

There has been to much UHI and political motivated correction of the data sets. First one will have to remove these before one can use the data to find a correlation with anything.

Before the previous. IPCC report the Jones et al showed the arctic North of 70 deg North to have been as warm or warmer in the 1930s. That changed drastically overnight with a new data set.

If arctic really is now warmer than in the 1930s, then I find it strange that the global temperature is much higher than in the 1930s. UHI and political motivated corrections?

As I suspected a notable circa 22y peak and “11y” is split into fine structure as is pretty much universally found, this is not a single peak. Closer read-off here gives : 10.12 and 11.22 y.

The largest peak is at 170y and is ten times the magnitude of the peaks show in this detail.
Usual caveats about data length etc apply. But that was the long period found in SSN in the chinese paper featured yesterday on WUWT. It appears also in cross-correlation with global SST as the strongest signal.

I’m curious that peaks appear to be close to being multiples 5.5 11,22,33,44,66

“….smaller secular variations in solar strength can have a significant effect on the climate…”

I have a problem with the words ‘smaller’ and ‘significant’ …… ‘smaller’ is the TSI variation, because of the huge output of the Sun….. the effect ‘significant’ only for humans who want to be comfy in a ‘narrow’ range of climate…. at the moment global warming is ~ 0.5deg extra….. well 300K or 300.5K ….. bugger all difference …. but ‘significant’ for humans….

The correlation between Earth Sea & Land Temps is from 22 year Magnetic Cycles NOT 11 year periods, why do you insist to show the 11 year Solar Cycles when it doesn’t show/prove anything as you have now demonstrated. Wills please go back to the drawing board and re work this article, I’m sure your find what your looking for

myline=strsplit(discrets(as.vector(spotgauss),collapse=T),split=””);myline (line 43 of shavivcorrelations.R) made my RStudio squawk. I couldn’t see a discrets() or discretes() function in your Willis Functions, and a search showed some possibles discretes() in the ggplot package.

Willis
Have you looked at this ?
Graph below is a detrended historical plot of the sea surface temperature anomalies (HADSST3) for the Pacific and Atlantic Ocean basins from pole to pole The peaks and valleys of this plot match the peaks and valleys of global atmospheric cooling and warming periods over the last 130 years . The surface temperatures of these oceans have peaked and are again heading for a cold trough by about 2040/2045 like they did 1910 and 1975. A global warming peak like we recently had is not predicted for 65- 70 years or until 2075/2080. The source of this cooling is the Global oceans Meridonal Overturning Circulation or MOC When there is a stronger than normal MOC, there is more deep cold water upwelling into the oceans by means of ocean conveyor belts. This will ultimately cool the SST and cool the Arctic as is already happening.

If we hind cast the above ocean graph and in particular the 70 year Atlantic Ocean SST, pole to pole , we find that major SST troughs like 1905/1910 and 1975 could have also happened in 1835, 1765, 1695 and major peaks in SST like 2010 and 1940 could have happened in 1870, 1800, 1730 and 1660.

For example the North Atlantic Ocean may have been cooling during the following past periods [And probably the Pacific as well.] The major solar minimum period is also noted

These cooler Atlantic Ocean SST periods correspond to the historic low CET temperatures and just happen to occur during the Maunder, Dalton and Modern Minimums of 1645-1715, 1790- 1820, and 1880-1910. In another words the reason for the low CET temperatures could have been the cool Atlantic SST and not because of the changing solar cycle during each of the three major solar minimums.

This changing Atlantic Ocean pattern can be seen in this Reconstructed North Atlantic SST between 1567 and 1990 with the courtesy of Bob Tisdale’s web page

RACookPE1978 says
But!!!! I do NOT know what will happen due to that change.
So, to cover for that lack of knowledge, I would prefer to focus on the earlier longer-term 66 year patterns of “several high, several low” cycles we see since 1650 as the earth warms from the LIA. Do those cycles matter?

henry says
we can measure the change if we take a balanced sample of weather stations of the world as shown earlier up this thread, and determine the change in temperature per annum
(which Greg also refers to)
e.g
Here are my latest results for the change in the speed of minimum temperatures (27 weather stations NH + 27 weather stations SH, balanced to zero latitude and 70/30 @sea/inland)
last 40 years (from 1974) +0.004 degree C/yr
last 34 years (from 1980) +0.007 degree C/ yr
last 24 years (from 1990) +0.004 degree C/yr
last 14 years (from 2000) -0.009 degree/yr

Now, note that the observed values are very low, indeed, yet it seems they are significant.
Namely, setting the periods out against the speed of warming/cooling I get a binomial again with rsquared equal to 1 (100% correlation)
There is no error in the equation…..
Hence, there is no AGW… There is no room for it in my equation.
One is tempted to think that we can project on this binomial forward, which would imply more cooling coming up ahead. Indeed, I think some more cooling is still coming up ahead. But we know from various investigations of mine, including ozone increase and the evaluation of solar magnetic field strengths, that we must come to a dead end stop on that binomial. Everything points to 2015 or 2016 as the date for the dead end stop of the process that causes the cooling on earth (coming from a variance of the sun’s output)

which suggests a 87 or 88 – and a 208 year cycle when we look at energy coming in
e.g. also
Persistence of the Gleissberg 88-year solar cycle over the last ˜12,000 years: Evidence from cosmogenic isotopes

Peristykh, Alexei N.; Damon, Paul E.
Journal of Geophysical Research (Space Physics), Volume 108, Issue A1, pp. SSH 1-1, CiteID 1003, DOI 10.1029/2002JA009390
Among other longer-than-22-year periods in Fourier spectra of various solar-terrestrial records, the 88-year cycle is unique, because it can be directly linked to the cyclic activity of sunspot formation. Variations of amplitude as well as of period of the Schwabe 11-year cycle of sunspot activity have actually been known for a long time and a ca. 80-year cycle was detected in those variations. Manifestations of such secular periodic processes were reported in a broad variety of solar, solar-terrestrial, and terrestrial climatic phenomena. Confirmation of the existence of the Gleissberg cycle in long solar-terrestrial records as well as the question of its stability is of great significance for solar dynamo theories. For that perspective, we examined the longest detailed cosmogenic isotope record—INTCAL98 calibration record of atmospheric 14C abundance. The most detailed precisely dated part of the record extends back to ˜11,854 years B.P. During this whole period, the Gleissberg cycle in 14C concentration has a period of 87.8 years and an average amplitude of ˜1‰ (in Δ14C units). Spectral analysis indicates in frequency domain by sidebands of the combination tones at periods of ≈91.5 ± 0.1 and ≈84.6 ± 0.1 years that the amplitude of the Gleissberg cycle appears to be modulated by other long-term quasiperiodic process of timescale ˜2000 years. This is confirmed directly in time domain by bandpass filtering and time-frequency analysis of the record. Also, there is additional evidence in the frequency domain for the modulation of the Gleissberg cycle by other millennial scale processes.
end quote

There is something fascinating about science. One gets such wholesale returns of conjecture out of such a trifling investment of fact.
Mark Twain
US humorist, novelist, short story author, & wit (1835 – 1910)

There is a rather broad peak in correlation at a lag of about 10 years and an equally strong, but more focused correlation at just over 21 years. This explains why Willis’ extension of the 11y “folding” idea shows nothing of interest. It does disprove either the circa 11 or 22y cycles or the presence of a solar signal. In fact is was a fairly sure way to destroy what is there. ( This was not Willis’ own idea, he was extending a technique used by Nir Shaviv. )

It should also be noted that is a clear anti-correlation with period of about 140 years and a lag of half that. That is a period of time, there is not enough data to suggest this is periodic as in cyclically repetitive.

Even this cursory investigation seems enough to suggest there is a correlation between SST and solar activity represented by SSN.

Willis, I am living on a Sailboat in the Bahamas. The surface temperature as measured by my IR gun for the entire last week was 80.4˚ F. And today since the wind has quit blowing the Surface temperature jumped to 84.6˚ F. The surface temperature is incredibly consistent 24 hours a day as long as the winds are consistent.

The ocean surface temperatures are controlled almost entirely by windspeed (evaporation rate). Clouds, Sunlight, clear sky, and night have no effect on the surface temperature.

Sure there are other things that affect the surface temperature, length of day, currents, rain, barometric pressure, but they are minor. The dominant factor is the wind.

If you think about it, this observation explains the UHI, Global warming and disproves climate change.

Despite being a regular reader here and at other climate blogs, I’ve not before come across the correlation between solar irradiance and poleward energy flux that Botkin recently put up as evidence to the Senate (of solar influence). I assume this is related to the (paywalled) paper below? (all I could find on a quick search). I’m not a climate dude so haven’t the tools to investigate. Have you come across this before and do you have any opinion on the apparent correlation? I can see by eye a couple of obvious deviations, but these could be volcanoes or something. No worries if I’m opening another can of worms you don’t want to look inside of ;)

Update: In response to an inquiry from a CA reader, Holgate gave the following response on solar/sea level connections:
Holgate’s response:
Many people have tried to link climate variations to sunspot cycles. My own feeling is that they both happen to exhibit variability on the same timescales without being causal. No one has yet shown a mechanism you understand. There is also no trend in the sunspot cycle so that can’t explain the overall rise in sea levels even if it could explain the variability. If someone can come up with a mechanism then I’d be open to that possibility but at present it doesn’t look likely to me.
If you’re interested in solar cycles and sea level, you might look at a paper written by my boss a few years back: Woodworth, P.L. “A world-wide search for the 11-yr solar cycle in mean sea-level records.” Geophysical Journal of the Royal Astronomical Society. 80(3) pp743-755
You’ll appreciate that this is a well-trodden path. My own feeling is that it’s not the determining factor in sea level rise, or even accounts for the trend, but there may be something in the variability. I’m just surprised that if there is, it hasn’t been clearly shown yet.

IPCC AR5 Figure 13.6
According to satellite telemetry GMSL between 2005 and 2012.5 increased about 20 mm. That’s about 0.75 of an inch. At that rate the sea level will increase another 8.25 inches by 2100. That won’t be a problem for anybody not dumb enough to currently live or build 4 inches above GMSL. Might need your high water pants.

Richard Verney says:
When considering this, one has to bear in mind that the heat flux is upwards in the first few millimetres of the ocean such that energy absorbed in the first few microns cannot find its way downwards by conduction

I was just wondering, as water is at it’s densest at 4C, could this make any difference, perhaps nearer the poles?

I would think that the Sun would act mainly through its interactions with our oceans; primarily with UV photons that get by the ozone layer. Visible will also contribute. During the decline of the last solar sunspot cycle, UV radiation declined 6% which should have an impact on ocean temperatures. This mechanism, because of the slow response time of these vast reservoirs, will not respond quickly enough to reflect Sun spot cycles, thus, there would be observable sun spot pattern. .

Sea Surface Temperature vs Integral of Total Solar Insolation
Willis. Try comparing Ocean Surface Temperature vs the INTEGRAL of TSI (or sunspots) rather than directly.
Frederick Michael asks: “Shouldn’t the sunspots . . . drive the FIRST DERIVATIVE of temperature, not simply the temperature?”
Greg Goodman affirms: “If there is an effect it should accumulate but integrating with the huge capacity of the oceans will great smooth out any signal”.
See David Stockwell who models and quantitatively shows temperature varies not directly but as the integral of solar flux, with a phase lag of Pi/2 (90 degrees) i.e. 2.75 years lagged from the ~11 year sunspot cycle.
Stockwell shows that the direct correlation of solar irradiance with temperature R^2 is only 0.028 while the cumulative solar irradiance has a correlation R^2 of 0.72 and solar + volcanic has R^2 of 0.78. See Fig. 4 in
David R.B. Stockwell “On the Dynamics of Global Temperature” August 2, 2011 http://vixra.org/abs/1108.0004
David R.B. Stockwell, “Accumulation of Solar Irradiance Anomaly as a Mechanism for Global Temperature Dynamics” 9 Aug. 2011

Here is presented a novel empirical and physically-based auto-regressive AR(1) model, where temperature response is the integral of the magnitude of solar forcing over its duration, and amplification increases with depth in the atmospheric/ocean system. The model explains 76% of the variation in GT from the 1950s by solar heating at a rate of $0.06\pm 0.03K W^{-1}m^{-2}Yr^{-1}$ relative to the solar constant of $1366Wm^{-2}$.

Firstly, variations in global temperature at all time scales are more correlated with the accumulated solar anomaly than with direct solar radiation. Secondly, accumulated solar anomaly and sunspot count fits the global temperature from 1900, including the rapid increase in temperature since 1950, and the flat temperature since the turn of the century. The third, crucial piece of evidence is a 90 deg shift in the phase of the response of temperature to the 11 year solar cycle. These results, together with previous physical justifications, show that the accumulation of solar anomaly is a viable explanation for climate change without recourse to changes in heat-trapping greenhouse gasses.

Stockwell further shows a 2.75 year Phase Shift in Spencer’s Data
Sunspots are an approximate measure of “Total Solar Insolation”.
Richard Verney makes another critically important point in insolation AFTER cloud effects – where there is very sparce uncertain data. David Stockwell highlights the importance of the insolation temperature phase lag to address Spencer’s challenge on whether:

1. Changes in cloud cover actually do drive changes in global temperature due to gamma-ray flux (GRF) or other effects, or
2. The changes in cloud cover are caused by changes in global temperature, with the derivative mechanism described above.
3. Both 1 and 2.

CO2skeptic’s note on the 22 year Hale cycle instead of 11 year Schwab cycle may be significant.

richard verney says:
June 7, 2014 at 12:49 am
————————————
You raise valid points regarding ocean spray and near surface saturated air. However while this H2O stops most DWLWIR from the atmosphere reaching the ocean surface, it is itself emitting LWIR back to the surface. There are two questions here, the effect of incident LWIR on the cooling rate of water and the effective emissivity of water.

Empirical experiment proves DWLWIR cannot heat nor slow the cooling rate of water that is free to evaporatively cool. That’s game over for DWLWIR slowing the cooling rate of 71% of Planet Ocean’s surface. You say “sorry Willis”. You wouldn’t say that if you saw what he wrote at Talkshop in 2011. Willis did not lose the debate to me. He lost to a roll of microwave safe cling wrap. That’s just sad.

And the effective emissivity of water? This is one of the church of radiative climastrology’s greatest “mistakes”. I have recently run some IR measurement experiments of warm water under a cryo cooled “sky”. An emissivity setting above 0.95 works well for measuring water temp in a sea of environmental IR. Eliminate this background IR and I find that you need an emissivity setting below 0.8 for accurate reading. I am beginning to suspect the old texts claiming IR emissivity of 0.67 may be correct.

What does this mean?

The ability of water to absorb UV/SW/SWIR is around 0.92. It’s ability to radiate LWIR could be as low as 0.7. Without evaporative cooling our oceans would become a giant evaporation constrained solar pond with temps topping 80C. The atmosphere is provably cooling 71% of the planets surface.

Someone will say an increase of neutrons of a few percent is not much. Is it really?
Primary cosmic rays are a stream of particles with energies from 10^7 to about 10^20 eV reaching the vicinity of the Earth from interstellar space. It consists of different types of particles: electrons and positrons, protons, alpha particles and heavier nuclei (up to uranium) and gamma quanta with high energy. This radiation does not reach directly to the surface of the Earth and can be seen above the Earth’s atmosphere on satellites or balloons.
The particles of primary cosmic rays entering the Earth’s atmosphere to produce particles called avalanche. secondary cosmic radiation, which is part of the natural radioactivity observed on the surface of the Earth.
Secondary cosmic rays reaching the Earth’s surface consists mainly of muons. Muons are unstable elementary particles very similar to electrons but 200 times heavier. Formed in the atmosphere mainly from the decay of another type of elementary particles, mainly risers and kaons. These in turn are created in the collisions of particles of primary cosmic rays with the atmosphere or the collisions of secondary particles created in the previous collisions.
The muons have an average life of at rest about 2 * 10^-6 seconds (2 microseconds). The muons decay into electrons or positrons and neutrinos. Muons are very penetrating particles (eg if they have energy above 5 billion electron volts, can penetrate more than 10 meters under the ground). With the electric charge can be easily recorded. These are ionizing particles.
The intensity of muons at the surface is about 200 particles per area of ​​1 square meter per second. This corresponds to the passage of particles about 6 per second by the head of a human, resulting in the head ionization approximately 100 million per second. It is natural radiation in our environment!

For those readers with a background in signal processing (especially geophysical processing), please take the time to read this post.

There is a gut feeling among many observers that the sun & solar cycles is somehow connected to climate. A reasonable hypothesis to want to explore , given that the sun ultimately powers our atmosphere.

Willis & others have shown that a solar signal isn’t readily discernible is various atmospheric / oceanic data sets. Perhaps the problem is the assumption of some sort of direct correlation.

Maybe a better mathematical model is a convolutional model ? The time series of some measure of solar activity would be the input signal. This would be convolved with an atmospheric “filter operator” with the output being an observed time series of atmospheric temperature. Since we have both the input signal (solar activity time series) and output series (atmospheric temperature time series), in theory , we could use a deconvolution process to solve for an atmospheric filter response operator.

This operator could then be tested against other datasets for viability in hindcasting and perhaps modified to come up with a better operator with better hindcasting ability. If the process worked, it could establish a connection between solar activity & temperature not readily visible through correlation or cross-correlation or any other spectral based approaches (because the spectra of the output signal would be different than the input signal, filtered by the atmospheric operator).

The spectral characteristics of the atmospheric operator may , in and of itself , provide deep insight into big picture atmospheric processes which have yet been unrecognized. There is an assumption that the operator is stationary but that could also be tested by deriving operators for different time periods. Perhaps there would be predictable variations in the operator with time. that would be useful in making forecasts of future temperature.

In short, there is a ton of potential research that could be done around the convolutional model & there is no telling just what insights could be gleaned until the research is done.

For any geophysicists out there, I am sure you can see the analogy here with seismic data. Input is your seismic source (analog = Solar activity time series) which is convolved with the Earth filter (analog = atmospheric filter ) , resulting in your observed seismic time series trace (analog = observed temperature time series).

I am not sure if anyone has pursued this type of research. I have always wanted to do this, but I simply do not have the time to pursue it. I would love it if someone would pick this idea up & run with it. it may work, it may not but I would be very curious to see the results. If someone chooses to pursue this idea based on this post, please just acknowledge where the idea originated.

Stockwell : “Spencer argues that it is impossible to distinguish between 1 and 2. Both Spencer and Lindzen both consider the lags important because correlation is greatly improved (and determines whether feedback is positive to negative). Neither seem to have mentioned the 3 month phase relationships emerging from integral/derivative system dynamics. I can’t see how it is possible perform a valid analysis without this insight. ”

Willis,
I would propose that if you are right, that the temp has a significant regulating mechanism, then any correlation between SSN and any temp set would be very difficult to find as long as there is sufficient energy into the system. Obviously it occasionally gets pretty cold here on earth we have plenty of evidence of that and it gets a tiny bit warmer, there is some evidence of that. So I would propose that we wont have any statistical evidence of sun influence until we enter a period of significant temperature change. .8C just isn’t enough to be detectable. Just my thought. Love your work by the way.
v/r,
David Riser

Where is the climate dataset that shows the ~11-year sunspot/magnetism/cosmic rays/solar wind cycle?
============
Perhaps it doesn’t exist because you are looking at the wrong information. the strength of the cycle varies inversely as the cycle length, and temperature integrates this signal.

thus, you will not see correlation at 11 years, except by accident. instead look for correlation between cycle length and inverse rate of temperature change.

ie; shorter cycles make it more warmer, longer cycles make is less warmer, as compared to average length cycles and historical trajectory of temperature.

1) Could the shape of the impulse response be applied for other volcanic events & associated AOD changes (or is each volcanic event have it’s own impulse response dependent on size of eruption, composition of ejecta, height of eruption, geographic location, etc)?

2) Could the same principle be applied to other forcings, such as solar variation or GHGs, the big difference being that the forcing isn’t a time spike like a volcanic eruption but a longer, time varying input ? (and of course , would it be possible to untangle various forcings or alternatively developing a net forcing time series & and figure out what the atmospheric filter is … if it does in fact exist).

It is encouraging that a convolutional model can be applied to AOD with meaningful results. it does suggest a convolutional model could be applied to other forcings as well.

Maybe a better mathematical model is a convolutional model
==========
for example, most would agree that the distance traveled in a car is related to the gas pedal. But often when the gas pedal is pressed hardest you are barely moving, and other times when your foot is off the gas is when you are traveling the fastest. So if one looks for simple correlation it may not be at all obvious that a relationship exists..

“I would propose that if you are right, that the temp has a significant regulating mechanism, then any correlation between SSN and any temp set would be very difficult to find as long as there is sufficient energy into the system. ”

There is strong regulation by negative feedbacks in the tropics, less so outside the tropics. That is why it was not difficult at all for me to find the correlation in the first SST dataset I used.

There is a clear 10,11,21 year peaks in the power spectrum . There is also a very strong correlation on the centennial scale the peaks at a lag of about 15 years. This tells us something about the degree of temporal accumulation and the depths of water involved.

There are multiple, significant correlations present but anyone expecting a nice simple 11y pattern, invariant over centuries, to jump out and hand itself up on a plate is being incredibly simplistic, and showing little comprehension of the complexities of climate ( and solar ) variability.

The approximately 11 year solar cycle is the witnessed cyclic variation of solar characteristics caused by internal solar processes. It is impossible that these variations can have zero affect on the Earth and in fact all the planets. If that affect is undiscovered it is because we’re looking for the wrong evidence or it is lost in the noise. I would suggest that the entire affect is spread across so many regimes that it is lost in the noise. If one does not understand the mechanisms of conversion of solar cycles to phenomena and have available instruments to observe and validate those phenomena, the affect can never be discovered. That is were we are.

The consequences of 11 year solar variation are hidden in plain sight and we may never be able to isolate them. I think too that an 11 year cycle is unimportant because: there is no accumulative impact that we can identify, and, we have bigger fish to fry.

What we do see is correlation between variations in the ~11 year cycle over time to time varying terrestrial phenomena and that is important.

Once you have the system response it should be applicable to all “forcings” in similar circumstances. The nice thing about Mt P was that it was big enough to be distinguishable for all the rest that is going on. This at least gives us an estimation of how the system responds.

After that it can be applied to smaller, faster or slower variations. Note what I did was based on tropical data so should be applicable to tropical response to other equatorial volcanoes. It should be applicable to calculate the tropical response to slowly increasing GHG forcing and/or solar variability.

I think this is how the question should be being investigated. I’m still a little uncertain of things like at which point the additional SW input that is seen since AOD settled, starts to take effect. That is why it’s still labelled as a draft version.

The extra-tropical AOD data seems less complete which is why I have not gone into that yet.

I see two things from that so far, the relaxation model indicates earlier values of AOD forcing from 1992 that were based on atmospheric physics were correct and have just been jerry-rigged since to make the models work better without changing the _preconceived_ assumptions about positive feedbacks. Secondly, that there are strong negative feedbacks in the tropics which take out just about any “forcing” changes within a year or two.

A third thing which came out of that was the warming effect of volcanoes that apparently no one has “noticed” yet.

dp says : “The consequences of 11 year solar variation are hidden in plain sight and we may never be able to isolate them. I think too that an 11 year cycle is unimportant because: there is no accumulative impact that we can identify, and, we have bigger fish to fry. ”

HUH? I just have. I’ve also ‘isolated’ a centennial scale signal that kinda needs to taking into account before wetting the bed every night for fear that they sky will burn tomorrow.

This seems plain to me. If the data has to be juggled, folded, sieved, lagged, stretched, pulled, and cut into bite size pieces of taffy in order to see a solar affect, wouldn’t that lead one to conclude that natural noisy intrinsic variation swallows a solar affect so completely that it can be appropriately disregarded in the search for a temperature trend driver? Am I missing something?

The Earth, with its many ways of storing and belching heat, seems quite capable of hourly, daily, seasonally, and long termally (backdoor alliteration is more fun than plain ol’ alliteration) changing the temperature all by itself-ally.

@Jeff L BTW the same principal should be applicable to work out CO2 / SST relationship. Rising SST will cause a degree of out-gassing. I don’t think anyone has properly assessed this yet. Murray Salsby sounded like he had something to present but never did.

He was sounding like he was saying it was all due to out-gassing which I think is improbable, just as improbable as ignoring it being the right answer ;)

The interplay of the both orthogonal in-phase responses are equally important. Neither can be left out.

Something I have heard of affecting weather is the 22 year Hale cycle, which the 11-year cycle is a half-cycle of. The sun’s magnetic polarity flips one way during one 11-year cycle, and flips back during the next. One thing that seems to happen every other minimum of the 11-year cycle is notably harsh winters from eastern North America to northwestern Europe. This does seem to be mostly a regional effect, whose impact on global temperature datasets is likely to be insignificant.

Another solar variation issue could be the mass of the oceans smoothing out the 11-year cycle more than longer period ones.

This seems to reflect the more or less triangular up and down ramps in the cross correlation function. This reminds me of the “acceleration” reported in the recent Jevrejeva paper on global sea level, that turned out be not a long term acceleration as suggested in the abstract but a rather sudden change of direction around 1850-1870.

Add the 130-140 year period of the SST/SSN correlation to that and we see it points to the present. May be something to look into.

This was a good, straightforward focused analysis on a single research hypothesis. If sunspots affect earth surface temperature, the effect appears somewhere else. It is helpful sometime to absorb simple results before trying to rescue the research hypothesis by rewriting it.

Greg I’ve read your stuff. Your stuff actually prompted the allegorical back-door alliterated reference to making taffy. The ingredients are the same beginning to end. The taffy shape and texture is what humans do to boiled sugar and water.

Earth’s climate and weather beginning to end is boiled sugar and water. Solar enthusiasts love to manipulate it to make it look different than what it is: a piled mound of boiled sugar and water.

Nicholas Schroeder, I have two IR Guns, both from Lowes the cheaper version and the more expensive one, Southwire is the name brand and yes they are very accurate when compared to the engine gauges, refrigerator, oven, engine intake, exhaust, water salinity gauge, cooking thermometer, etc. and yes mercury gauges. It is amazing how many thermometers I have on board.

The bottom line though, and I have tested it with the cooking thermometer, the surface temperature of the ocean does not change due to the daily solar cycle, clouds, lack of clouds, etc.

Direct solar radiation, not to mention back radiation has zero effect on the surface temperature. The only effect insolation has on the surface temperature is on how quickly the surface temperature responds to changes in wind speed.

I have been studying this for two years now from the States down through the Bahamas, to the Bottom of the Caribbean. I am currently in Georgetown and the surface water temperature is 82.2 degrees and it was 82.2 degrees this morning before dawn. We had a little squall come through and the temperature dropped a few degrees while it was raining, but immediately jumped back up after the squall passed.

I just wanted to lend my support for the integral/derivative relationship between flux and temperature. I have had success modeling the integral of flux vs. temperature, and it’s a reasonable assumption.

Computationally, there are benefits to integration vs. derivatives (the integral is just the sum), but mathematically, the fundamental theorem of calculus ensures us that we should be able to go either way.

So, let’s play the ‘maybe sunspots-co-relate-to-something-else-in-the-sun-that-might-affect-global temperatures on earth over long periods of time game. It does make sense somehow, but we may not (actually really and absolutely do not know what) that relationship might be.

Look again above at the plots.

Add a lag.
Look not a 11 year cycle, but a six-sunspot 11 year (33 positive/33 year negative) 66 year cycle of alternating “positive” and “negative” cycles that themselves are near-equal, but over a three set cycle may mean something important .

Thanks, RA. I’m looking at the 11-year cycle for a specific reason—it should be the most visible cycle, because the longer (e.g. 66-year or 102-year or whatever) cycles are all much smaller than the 11-year cycle.

The problem facing your and other similar hypotheses is explaining why the earth should respond to a comparatively weak e.g. 66-year solar cycle, while at the same time not detectably respond to the much stronger 11-year cycle. We know that the answer is not “thermal inertial”, because the temperature of the ocean can and does change radically over the period of a couple months. So there is no “thermal mass” impediment to responding to an 11-year cycle.

Once you’ve solved that question, and can show (not claim but show) why the earth should respond to a combination of 11-year cycles, but NOT to the 11-year cycles themselves, your hypothesis will be worth investigating. Until then, I’ll continue to look for the traces of the largest and clearest solar cycle in the climate data … and that’s the 11-year sunspot cycle.

If Willis finished the job and did a FT of the correlation he performed, he probably would have found a similar result. The degree of correlation was probably not helped by Shaviv’s choice of an over-processed, interpolated “ice and sea” dataset but just by eye I could see enough to suspect what I found when I looked in detail.

Ferdinand, I’m getting tired of picking spitballs off the wall. The link you gave CLAIMS a relationship between the ICOADS dataset and sunspots. It doesn’t do a single lick of math. Not one. It doesn’t do any analysis. Not a bit. Nor does it provide a single link to the underlying datasets. It is a very poorly designed lesson plan, obviously done by someone who is not a teacher, aimed at the eighth-grade / high school level.

I am not interested in people coming up with every person on the internet who has made a claim about the sun. I am interested in actual analyses.

If you think that such links are worth following, then I strongly encourage you to follow them. Go and get the ICOADS data, do the analysis, and THEN come and tell us about it. But don’t bother me with your data dredge of everything on the internet that comes up when you google “sunspots sst”. I followed your first link, and it was a total waste of my time. Won’t make that mistake twice.

Willis states, “What I am saying is that I still haven’t found any convincing sign of the ~11-year sunspot cycle in any climate dataset, nor has anyone pointed out such a dataset.”

Zhou & Tung (2013) found the cycle in their data set, ie tropospheric temperature observations. In a prior post, I read your questions about this paper, apparently based upon its abstract, but IMO the whole article is worth actually reading. Doing so would help answer your questions & respond to your general objections to “reanalysis”:

“The article you are purchasing is protected by United States copyright law and may not be reproduced, distributed, displayed, or republished without the prior written permission of the Publisher and/or the copyright holder. The copyright holder is indicated on the front page of the article.”

The presence of a longer term periodic change would also explain why the 11y “folding” (which is a pretty inappropriate term since it implies some kind of reversal) ends up with just noise. If the long period is near 22y using that as the offset may be more appropriate.

I don’t understand this argument. Why would the climate respond to a weak 22-year signal but not to a strong 11-year signal? In any case, I’ve given you the data, so assuredly you’ve done the 22-analysis before opening your mouth. Show us your magic 22-year folding and how well it works.

Indeed. The primary effect of a radiative forcing is dT/dt as can be seen by the physical dimensions. Radiation flux is power ; temp is energy. They are orthogonal, so the initial investigation should be rad (SSN) vs temp.

Greg, as I recall you’re a smart guy … smart enough to know that such speculation without backup is useless on my threads. If you think that using dT/dt will show a better result, or that there is a 22-year cycle, then get up off of your moribund okole and demonstrate that to us.

I can’t tell you how tired I am of people with big mouths, beautiful theories, and not one dang thing to back them up. As I said in the head post, this is not the place for your and everyone else’s oh-so-brilliant speculation and oh-so-wise objections. If you think you can find the 11-year signal using dT/dt, then stop talking about it and GO DO IT!

As I suspected a notable circa 22y peak and “11y” is split into fine structure as is pretty much universally found, this is not a single peak. Closer read-off here gives : 10.12 and 11.22 y.

The largest peak is at 170y and is ten times the magnitude of the peaks show in this detail.
Usual caveats about data length etc apply. But that was the long period found in SSN in the chinese paper featured yesterday on WUWT. It appears also in cross-correlation with global SST as the strongest signal.

I’m curious that peaks appear to be close to being multiples 5.5 11,22,33,44,66

A fourier analysis of a cross-correlation? Perhaps you could explain what that is supposed to show. Serious question, I just don’t understand the procedure. For example, try running cross-correlations of the sunspot data with random red noise … you’ll get peaks at ~11-year intervals. A fourier analysis will indeed show those 11-year cycles … but so what? That’s just what you get when you run a cross-correlation of sunspots with almost anything.

Also, you say that you find strong periods out to 170 years … but according to the data link that you attached to the graph (many thanks, that’s the mark of science), the data you are analyzing looks like this:

Gotta say, Greg, when a man claims he can detect a strong 170 year cycle from data that looks like that, I cease to take him seriously. That doesn’t pass the laugh test.

myline=strsplit(discrets(as.vector(spotgauss),collapse=T),split=””);myline (line 43 of shavivcorrelations.R) made my RStudio squawk. I couldn’t see a discrets() or discretes() function in your Willis Functions, and a search showed some possibles discretes() in the ggplot package.

Thanks, David. As I said, I’m glad to answer questions about my code, because I encourage people to run the data themselves. The “discrets” function is in the package “seewave”, which I thought was called by

require(seewave)

in the first couple of lines of the program. If not, you’ll need the seewave package.

w.

PS—To find the answer to such questions, I often google something like “discrets cran R”. Including “cran R” restricts the search and will generally find what I’m looking for.

Willis
Have you looked at this ?
Graph below is a detrended historical plot of the sea surface temperature anomalies (HADSST3) for the Pacific and Atlantic Ocean basins from pole to pole The peaks and valleys of this plot match the peaks and valleys of global atmospheric cooling and warming periods over the last 130 years .

Thanks, herkimer. Since the oceans are 70% of the surface, of course the global temperature and the oceanic SST are very closely correlated … and? What does that have to do with the ~11-year sunspot cycle? What am I missing here?

It should also be noted that is a clear anti-correlation with period of about 140 years and a lag of half that. That is a period of time, there is not enough data to suggest this is periodic as in cyclically repetitive.

Greg Goodman says:
June 7, 2014 at 2:32 am

The largest peak is at 170y and is ten times the magnitude of the peaks show in this detail.
Usual caveats about data length etc apply.

If you disagree with someone, myself included, please QUOTE THE EXACT WORDS YOU DISAGREE WITH. This prevents many flavors of misunderstanding, and lets us all see just what it is that you think is incorrect.

OK, now you’ve evoked the envy response in 87.34% of your readership …

The surface temperature as measured by my IR gun for the entire last week was 80.4˚ F. And today since the wind has quit blowing the Surface temperature jumped to 84.6˚ F. The surface temperature is incredibly consistent 24 hours a day as long as the winds are consistent.

The ocean surface temperatures are controlled almost entirely by windspeed (evaporation rate). Clouds, Sunlight, clear sky, and night have no effect on the surface temperature.

Sure there are other things that affect the surface temperature, length of day, currents, rain, barometric pressure, but they are minor. The dominant factor is the wind.

I’ve pointed out the importance of the wind before, particularly as regards thunderstorms. The key piece of data is that evaporation rises linearly with windspeed. So if the wind goes from say 1 metre/second to 10 m/sec, the evaporation goes up by a factor of ten.

However, I wouldn’t say that the ocean surface temperatures are “controlled almost entirely by windspeed”. Particularly in the tropics, the clouds regulate the amount of energy entering the system. If you get a day with no clouds, SSTs will climb. On the other hand, a cloudy day followed by a clear night will lead to falling SSTs.

If you think about it, this observation explains the UHI, Global warming and disproves climate change.

Dang … that sounds like one of those old snake oil remedies, that cures lumbago, arthritis, and liver disease.

Sadly, the climate is not that simple. I fear there is no “magic bullet” that explains everything.

Regards,

w.

PS—You claim that something “disproves climate change”. Ask yourself what that actually means given that climate has been changing since there has been climate …

That is correct, and it will represent the strength of the 11 y component in both SSN and the red noise.
If you think a lagged cross-correlation is meaningless, don’t do it.

You throw a 0.2 “significance” line across your CC . Both my ICOADS and hadISST plots show 11 and 22 y lagged peaks above that level. Showing significance by the criterion you adopt. Yet you seem to think there’s not a significant signal.

Actually a more rigorous effort should be made since one-size-fits-all does not count for determining significant levels of correlation. I’ll see if I can come up with something since that should be on my plots too.

Using 0.2 everywhere is an error I’ve pointed out before that you have not addressed beyond trying to throw the ball back in my court and carry on using 0.2 whatever length and nature of data is.

Fair point, but no. You’ve used “at least 30%” default option. I should have stated in the description somewhere that I used “at least 2%” coverage, to get a fuller set. I also have a script which fills minor breaks.

So, fair pick, thanks for pointing it out. But no, the data I’m processing is not a broken mess.

…
Zhou & Tung (2013) found the cycle in their data set, ie tropospheric temperature observations. In a prior post, I read your questions about this paper, apparently based upon its abstract, but IMO the whole article is worth actually reading. Doing so would help answer your questions & respond to your general objections to “reanalysis”:

So smart I’d done it and posted it 9 hours before you told me “directly” to get off my butt and do it.
I’ll take that as an almost apology ;)

Without worrying about the FT of CC for the moment. What information do you think can be derived from cross-correlation. You used it to support your impression that there is no solar signal, so you must expect that something could be there that was not.

I’m guessing you were looking for a peak that is above your 0.2 threshold (but I don’t want to puts words into your mouth, so please correct me if that’s wrong).

At issue here, besides the lack of observation in the temperature anomaly, is the reluctance of solar enthusiasts to first show that there are no other potential drivers that can serve as the energy behind temperature trends.

There is, and one that correlates much better with land temperatures. That would be ocean heat and its teleconnection with atmospheric processes (IE ENSO systems, clouds, large and small varying pressure systems, sudden events that produce aerosols, etc). The mechanism behind this is the heat storing capacity of the oceans along with Earth’s own filtering systems in the atmosphere, a capacity that has more than enough variables attached to it to take a steady state (relatively speaking) source and absorb it in varying amounts, and then belch it back out in varying amounts. I see no need for the Sun to vary in order for that process to work.

Yes it’s very simple, the magnetic field of Jupiter, Saturn, the Sun and Uranus, modulate the magnetic field of the heliosphere and vary the amount of GCR impacts in the upper atmosphere of this planet and all others, and modulate the transparency. The 11 year sunspot cycle is nothing but an effect of the wobble caused by Jupiter alone, the longer term trends are associated with Saturn and Uranus and the way that they are coupled with the Suns magnetic field, and shield us from the galactic plane.

Subject: This post is about the quest for the 11-year solar cycle. It is not about your pet theory about 19.8 year Jupiter/Saturn synoptic cycles. If you wish to write about them, this is not the place. Take it to Tallbloke’s Talkshop, they enjoy discussing those kinds of cycles. Here, I’m looking for the 11-year sunspot cycles in weather data, so let me ask you kindly to restrict your comments to subjects involving those cycles.

The Dansgaard-Oeschger events have an imagined periodicity of 1,470 years, thus do not appear to be the 11-yr sunspot solar cycle.

Despite being a regular reader here and at other climate blogs, I’ve not before come across the correlation between solar irradiance and poleward energy flux that Botkin recently put up as evidence to the Senate (of solar influence). I assume this is related to the (paywalled) paper below? (all I could find on a quick search). I’m not a climate dude so haven’t the tools to investigate. Have you come across this before and do you have any opinion on the apparent correlation? I can see by eye a couple of obvious deviations, but these could be volcanoes or something. No worries if I’m opening another can of worms you don’t want to look inside of ;)

Willis
Since the oceans are 70% of the surface, of course the global temperature and the oceanic SST are very closely correlated … and? What does that have to do with the ~11-year sunspot cycle? What am I missing here?

If it is not the various sun cycles that drive the 60-70 year atmosphere cycle then it is likely the Ocean cycles as Bob Tisdale has cleared shown . I tend to agree with Pamela Gray’,s post . For example the extra cold temperatures in the CET records during past major solar minimums may not be due to solar minimums at all but due to valleys in the North Atlantic SST due to stronger MOC which causes more upwelling of colder water. driven by the oceans conveyor belts . AMO has gone negative now for many months and the past 60-70 year SST pattern may again be repeating it self.

but [I think] you would have to try and link the 1470 year cycle to some special configuration of the solar system,
just like I did with the 87 year Gleiszberg cycle
(predict dead end strop in the middle of the cycle around 2015 or 2016)
Happy hunting!

Sorry, no joy with that one either. The cumulative sum (integral) of sunspots starts up high in 1880, drops steadily to a valley in 1940, and then increases steadily to 2013. No correlation at all to SST.

Guys, again I encourage you to actually look at the data before you uncap your electronic pen … don’t take some random guy’s word for it just because he got it published somewhere. I’ve met Nir Shaviv, he seems a good guy, I don’t think he has bad motives or anything … but I don’t take anyone’s word for anything. “Nullius in verba”, as the saying goes …

A reader on my infrequently updated blog pointed me out to your post here. Here are my thoughts about it.

You write: “… however, I’m not seeing it. So where can we find this mystery ~11-year cycle?”. So, let me start by referencing earlier work detecting the 11-year solar cycle in the land or sea surface temperature records. The following examples (and quite a few more I didn’t mention) indicate that the peak to peak variations of either SST or land surface temperature are around 0.08 to 0.1°C between solar minimum and solar maximum:

– Douglass and Clader (2002) found a pear to peak variaton of 0.11 ± 0.02K

Given these previous detections it is thus worthwhile to ask whether you expect to see a statistically significant signal in the dataset you used. i.e., you should place an upper limit and then compare to previous determinations of about 0.1°C. If the upper limit is bellow this value, then you have something interesting to say, otherwise, the result is isn’t interesting, it just means that the dataset you used doesn’t prove or disprove anything.

When inspecting your fig. 6, the signal you see there does in fact have the right amplitude and phase. So the question is not whether you see a signal or not, it should be whether the signal you see is statistically significant.

To answer this question you have to do some minimal statistical analysis which you haven’t done. For example, you should estimate the probability that the null hypothesis would give the blue line apparent in fig. 6, namely that a large fraction of the points would be as far as they are from the null signal. (e.g., by calculating the chi 2 and the effective number of degrees of freedom). I did this and found that the null hypothesis can be ruled out at better than the 99% confidence level. (and it doesn’t matter if I plotted 1-sigma or 2-sigma error bars on the points).

The two possible answers that you could get are that a) the null hypothesis can be ruled out, implying that this dataset supports the idea that you see a solar signal. b) the null hypothesis cannot be ruled out, which would then just mean that this dataset does not support the idea, but it doesn’t rule it out either. Now, given that there are other data sets that clearly show the solar signal, I couldn’t care less what the answer is.

Next, you don’t see a signal when using the SST from 1870. However, your conclusions don’t consider the following points:

a) The SST over long time scales is very poor, not much signal and a lot of noise. How much of the southern pacific was sampled by boats in the first 50 years of that time interval? In fact, even today it is poorly sampled.

b) You assume that the solar cycle is strictly periodic (say 11 years?) but in fact it isn’t. As a consequence, any analysis such as Fourier transforming, or folding over the period is smeared out from the non-constant period. (You have to fold the data while keeping the right phase within the solar cycle, which I don’t think you did).

c) Like with the previous case, you don’t estimate what is the statistical significance of the null result you find. Are the errors so large that the expected signal drowns in noise?

Last, my biggest grievance about your criticism is that the analysis in the paper you considered was of 3 datasets. You chose only the SST, and disregarded the tide gauge records which shows the solar signal with a very high statistical significance. See http://www.sciencebits.com/calorimeter .Its problem is that it may have some “contaminant” from variations in the amount of ice, but that doesn’t matter if you want to prove that you see a climatic signal over the solar cycle.

You could also ask whether 0.1°C is large or small, and the answer is that because of the large heat capacity of the oceans it corresponds to about 1W/m^2 variations (when averaged over Earth’s surface). So in fact, the small temperature variations observed are at leaf 6 times larger than what the IPCC is willing to admit.

“Willis & others have shown that a solar signal isn’t readily discernible is various atmospheric / oceanic data sets. Perhaps the problem is the assumption of some sort of direct correlation.”

PERHAPS THERE IS NO FRICKING PROBLEM

The sun varies by a small number of watts from peak to peak
The climate doesnt respond to these small variations.

AS IN DUH

occam says……

Thanks, Mosh. Most folks seem to know that, but instead they agree with Nir Shaviv when he says:

“We find that the total radiative forcing associated with solar cycles variations is about 5 to 7 times larger than just those associated with the TSI variations, thus implying the necessary existence of an amplification mechanism, though without pointing to which one.”

“The Earth, with its many ways of storing and belching heat, seems quite capable of hourly, daily, seasonally, and long termally (backdoor alliteration is more fun than plain ol’ alliteration) changing the temperature all by itself-ally.”

The solar variation is so minor that I do the following thought experiment.

I make a chart of TSI versus time.
I label it c02.
I make a chart of temperature versus time
I label it temperature.

Then I imagine what a skeptic would say If I claimed that chart one helped to explain chart two.

@nir shaviv
btw
my results [on the Gleiszberg cycle alone] suggest a variation of ca. +0.5 degree C max. in the warming period of 44 years and ca.-0.5 degrees C min in the cooling period.
On average this suggests an average difference of [0.125K] between each of 8 succussive Schwabe solar cycles .[I think] this compares well with your own result.
I am just a small unpaid hobbyist, you know….

Willis – “However, I wouldn’t say that the ocean surface temperatures are “controlled almost entirely by windspeed”. Particularly in the tropics, the clouds regulate the amount of energy entering the system. If you get a day with no clouds, SSTs will climb. On the other hand, a cloudy day followed by a clear night will lead to falling SSTs.”

No that is incorrect, and to me very surprising also. I have been measuring it (in the tropics too) and it simply doesn’t work that way. What does get warmer is the layer just below the surface. What happens is that the top warm layer gets thicker during sunny days and thinner during cloudy days and nights, but the surface stays the same temperature as long as there is a warm layer reservoir below it.

I know i sound like a snake oil salesman, but wind speed (via evaporation and conduction) really does control the surface temperature. And it explains everything else too :) Higher wind speeds = lower temperatures as long as there is moisture to evaporate of course, which is why it explains UHI, Climate change, climate warming, cooling etc. Wind is also self regulating, higher temps means increased evaporation which creates lows which increases the wind speed which lowers the surface temperature.

Unless changes in radiation levels can change the wind-evaporation loop changes in radiation levels won’t control the climate. That is also why everyone is searching in vain for correlations with sunspots, CO2, albedo, etc. they don’t exist. But humans are great at spotting patterns : )

“We have established statistically the existence of the 11-yr solar cycle signal in temperature throughout the troposphere. There is a robust heating center located over the tropics below the tropopause in all seasons, which is statistically significant. It cannot be interpreted as heating due to ozone absorption of solar UV radiation, since tropospheric ozone concentration is extremely
small. This heating is situated above a minimum in warming over the tropical ocean surface, suggestive of vertical convection caused by surface heating and evaporative feedback (which reduces surface warming). There are two vertical strips of warming outside the edge of the tropics in the troposphere that could be a result of a poleward shift of an expanded Hadley circulation.
The evidence we present here is suggestive of a ‘‘bottom up’’ mechanism for the tropospheric and
surface response similar to that for greenhouse warming, as discussed in Cai and Tung (2012): Most of the solar forcing reaching the surface in the tropics does not go directly into warming the ocean but into evaporating water and heating the upper troposphere through convection and latent heating. From there large-scale transport carries the heat poleward, resulting in a global
warming pattern. Because the tropical ocean is not warmed appreciably, only a small fraction of the heat is transferred into the ocean mixed layer. This may explain why the lag in the surface and tropospheric response is almost nonexistent, smaller than expected based on the thermal inertia of the entire mixed layer, and why the amplitude of the response is close to that estimated at
equilibrium.

“Previously, there have been several general circulation modeling studies with fixed sea surface temperature (SST) (Shindell et al. 1999; Haigh 1996, 1999; Larkin et al. 2000; Matthes et al. 2004; Balachandran et al. 1999) to isolate the ‘‘top down’’ effect. However, given that the visible/near-infrared solar heating in these experiments still penetrates to the surface, evaporates water,
and causes vertical convection, the calculated circulation change could still be, at least partly, from the bottom-up mechanism that we proposed. Since the change in the tropical SST is small in both the observation shown here and in the model of Cai and Tung (2012) where the SST was allowed to vary, fixing the SST in the important tropics in these experiments does not present a condition so different as to prevent the bottom-up mechanism from acting. Therefore, the results from these fixed SST experiments cannot be interpreted as arising only from the top-down mechanism.”

Not sure I consider model tests as experiments, but the approach is better than making a lot of assumptions not in evidence.

Nir, you do realize that papers that include “changes in solar irradiance” based on the uncorrected SSN values places you on shaky ground in terms of supporting literature.

A case in point: I wonder if Judith Lean still considers the paper linked below an adequate examination of solar drivers given what she now knows of problems with solar data (IE Leif’s group working on reconciling weighting issue with raw SSN). If my memory serves me, she no longer subscribes to solar reconstructions used in several papers she has co-authored in the distant past. Whether that includes this one I don’t know.

The Sun is about to enter a hibernation phase into solar cycle #25 and that will usher in global cooling, which I have been forecasting for years is coming. It is closer now and will begin officially in December 2017.

As for ENSO, it is driven, like all climate change on Earth, by the Sun and planetary angular relationships to the Sun and Earth.

However, there will be no ENSO this year, next year, or the year after that.

The next ENSO will be a powerful La Nina, that will impact 2020-2022, with the worst winter season in the northern hemisphere in 2021-2022, and it will rival the last brutal winter of 2014 and set new weather records for cold temperatures, heavy snowfall and ice.

In my climate forecast, there will be no ENSO until 2020, and that one will be La Nina – a very strong one at that.

Until 2020, we will see strange cool plumes in worldwide sea surface temperatures; a lack of hurricanes; along with the continued growth and expansion of the Arctic and Antarctic sea ice extents.

This is the trending to Global Cooling, which I have forecasted to begin officially in December 2017 and last approximately 36 years.

ENSO events are solar-planetary forced and occur every 10-11 years. The last ENSO, which I forecasted, was a El Nino in mid-2009 that was followed by a La Nina in 2010-11.

Think of ENSO as climate change in action.

You are seeing what amounts to a large scale variability in the circulatory system, and when you take out ENSO you are removing a climate mechanism where the thermal/kinetic exchange to equilibrium is achieved.

ENSO is externally forced through the polar annular modes/AAM, and ENSO is climate change in action. What confounds the computer modellers about ENSO’s cycle is that the thermodynamic response to perturbation is not linear.

ENSO responds to fluctuations by the external forcing from the Sun.

Understand at the dynamics of ENSO and what forces it.

ENSO is forced by the Sun externally because the strength of the trade winds, that’s Walker Cell dynamics, and the AAM integral come before ENSO SST variation.

Now, the atmosphere is the less energetic body, so by definition there has to be an ‘external’ perturbation present.

Evidence of such Solar forcing exists and the relationship is significant:

Corotating coronal holes of the Sun induce fluctuations of the solar wind speed in the vicinity of the Earth.

These fluctuations of solar wind speed are closely correlated with geomagnetic activity and the resultant geophysical climate and weather effects on Earth.

It is basic to Astrometeorology. That is what I do.

Now, solar wind speeds have been observed and monitored by orbiting Earth satellites since the mid-1960s. The long-term series of solar wind speed clearly reveals enhanced amplitudes at the solar rotation period of 27.3 days and at its harmonics 13.6 and 9.1 days.

The amplitude series are modulated by a quasi-biennial oscillation (QBO) that has a period of 1.75a (that’s 21 months) as bispectral analysis reveals.

A 1.75a QBO component is also present in the equatorial, zonal wind of the stratosphere at 30 hPa, in addition to the well-known QBO component at the period 2.4a (at 29 months.)

And the series of solar wind speed fluctuations are bandpass-filtered at the period 1.75a. The filtered series provide the amplitude of the solar wind QBO as function of time.

The maxima of the solar wind QBO series correlate with those of the ENSO Index. Analysis confirms that the solar wind QBO helps to trigger ENSO activity.

The solar forcing of ENSO is done by changes in meridional flux through the NAM/SAM and that ties directly right back into planetary wave action.

In volume 36, issue 17, of the September 2009 Geophysical Research Letters, Rodrigo Caballero and Bruce T. Anderson state that:

“Stationary planetary waves are excited in the mid-latitudes, propagate equatorward and are absorbed in the subtropics. The impact these waves have on the tropical climate has yet to be fully unraveled.

“Previous work has shown that interannual variability of zonal-mean stationary eddy stress is well correlated with interannual variability in Hadley cell strength. A separate line of research has shown that changes in midlatitude planetary waves local to the Pacific strongly affect ENSO variability.

“Here, we show that the two phenomena are in fact closely connected. Interannual variability of wave activity flux impinging on the subtropical central Pacific affects the local Hadley cell. The associated changes in subtropical subsidence affect the surface pressure field and wind stresses, which in turn affect ENSO.

“As a result, a winter with an anomalously weak Hadley cell tends to be followed a year later by an El Niño event.”

Moreover, there is a link from the Pacific Meridional Mode to ENSO, as Ping Chang and Link Ji from Texas A&M University at College Station, Texas wrote in late 2008:

“The occurrence of a boreal spring phenomenon referred to as the Pacific Meridional Model (MM) is shown to be intimately linked to the development of El Niño–Southern Oscillation (ENSO) in a long simulation of a coupled model.

The MM, characterized by an anomalous north–south SST gradient and anomalous surface circulation in the northeasterly trade regime with maximum variance in boreal spring, is shown to be inherent to thermodynamic ocean–atmosphere coupling in the intertropical convergence zone (ITCZ) latitude, and the MM existence is independent of ENSO.

“The thermodynamic coupling enhances the persistence of the anomalous winds in the deep tropics, forcing energetic equatorially trapped oceanic waves to occur in the central western Pacific, which in turn initiate an ENSO event. The majority of ENSO events in both nature and the coupled model are preceded by MM events.”

Now, the reasons why NOAA/NWS and every other conventional climate center on Earth, along with climatologists and their computer models cannot forecast ENSO; is that their computer models are shit.

ENSO is an *astronomically-caused* climate event.

And clearly the algorithms in their overblown and error-filled computer models are not programmed to understand ENSO.

That is why they cannot forecast it and every single year they come out with forecasts on ENSO and they fail.

They did it last time when I forecasted the 2009-2011 ENSO three years in advance, from 2006.

Rather, what conventional modellers do is that they take an initial condition and then they apply their own perturbation theories to attempt to get a future projection – and those projections are always wrong, wrong, wrong.

In truth, in the real world of climate, ENSO is NOT an internally driven or a chaotic phenomenon.

ENSO is a solar and planetary magnetically-driven event that forces upper stratospheric U-flow/QBO and you can witness the results and impact on the N/S annular modes.

Reports from the CFS project on the 2011 La Nina that I forecasted fell to -4C because those expensive computer models are founded on absolutely useless methods on the given boundary conditions that they use to project from.

It means that they are essentially using a system dynamic that *drives* the system state, rather than the other way around. They have it ass backwards.

For instance, if you subtract ENSO, then you also have to subtract the poleward migration of Hadley cells/expansion of the Ferrel cells seen since solar year 1976.

Now, once you do that, you will lose the 3-4 percent decrease that’s observed in tropical cloud cover. Therefore, you lose essentially all of the warming that has occurred since the 1970s and that relates to about 3.5W/m^2 of loss since 1982.

NOAA/NWS and every other climate forecast center do not successfully produce accurate seasonal forecasts.

Again, that’s because their models are only programmed to the general governing equations that are put into them.

For years now, with all that money they’ve wasted, the computer climate modeling world is a total disaster and they have to know it after busting every season, every year, year in and year out.

Again, there will be no ENSO until 2020. We will see signatures by mid-2019 when things really begin to get interesting, but by 2020 there will a full blown La Nina that will be in force for 2.5 years according to my calculations.

The worst of it will be during the winter of 2021-2022 – a really bad and long winter season followed by a cold, wet spring and cool summer of 2022.

ENSO is climate change in action and that climate change is to GLOBAL COOLING, which officially begins in December 2017. That’s been my forecast and people had better prepare for it too.

“What I am saying is that I still haven’t found any convincing sign of the ~11-year sunspot cycle in any climate dataset, nor has anyone pointed out such a dataset.”

Zhou & Tung (2013) found the cycle in their data set, ie tropospheric temperature observations. In a prior post, I read your questions about this paper, apparently based upon its abstract, but IMO the whole article is worth actually reading. Doing so would help answer your questions & respond to your general objections to “reanalysis”:

“The article you are purchasing is protected by United States copyright law and may not be reproduced, distributed, displayed, or republished without the prior written permission of the Publisher and/or the copyright holder. The copyright holder is indicated on the front page of the article.”

Pass. Their previous claim was Zhou-Tung-Camp 2008, where they claimed to find strong solar cycle / temperature signals in various temperature datasets.

Perhaps you could read that one, and tell us all how many complete solar cycles were included in their whiz-bang analysis? Because once I did that, I gave up entirely on Mssrs. Tung and Zhou. Not to mention the thrashing they gave to their data, cutting out chunks they didn’t like … sorry, milodon, but those guys are on my junk list.

HOWEVER: If you think Z&T2013 is valid, how about you do what I suggested? I mean, you’ve got the paper and I don’t. Go get their numbers and see if you can replicate their results.

Again, folks, let me invite you to do your homework. If you think something is valid, hows about YOU take the time to see if you are recommending garbage BEFORE tossing it on the table? I’m getting tired and bored with shoveling the muck.

How do you know it’s garbage if you haven’t read it? Ignoring others’ work or dismissing it out of hand doesn’t inspire confidence in your conclusions. Even if you don’t want to consider it, their references include relevant papers.

I don’t think it’s up to me to reproduce their results. It’s your quest to find a signal for the 11-year cycle, not mine. On its face, their methodology looks OK to me.

Courtesy of Kadaka, there’s now a link to Zhou & Tung (2013) in the comments above. I copied their conclusion section.

That is correct, and it will represent the strength of the 11 y component in both SSN and the red noise.

Not true in the slightest. If you take e.g. data that has only one large peak or one major trough in the data, it will have NO 11-year periodicity. This kind of single-peak or single-trough result is quite common in red noise.

But if you run a cross-correlation of that non-cyclical data with the sunspot data, you’ll get a strong 11-year cycle.

In other words, the procedure you are using will very frequently have an 11-year cyclicality REGARDLESS of whether such a cycle exists in the other dataset or not.

How do you know it’s garbage if you haven’t read it? Ignoring others’ work or dismissing it out of hand doesn’t inspire confidence in your conclusions. Even if you don’t want to consider it, their references include relevant papers.

I don’t “know that it’s garbage”, that’s why I suggested that you investigate it and see if it is or not.

What I was trying to say (apparently unsuccessfully) was that in my opinion, those guys are not worth wasting my time on. I’m simply following the first rule of holes.

I don’t think it’s up to me to reproduce their results. It’s your quest to find a signal for the 11-year cycle, not mine.

If you are citing their results and commending them to my attention, you are saying that you think that they are true and worthwhile of my spending time on.

On its face, their methodology looks OK to me.

Courtesy of Kadaka, there’s now a link to Zhou & Tung (2013) in the comments above. I copied their conclusion section.

If you think that the paper is worthwhile, go for it. But don’t try to bust me for being once-bitten, and therefore twice-shy. When two guys make ludicrous claims like they did in Z&T2008, they go to the bottom of my list of things to spend time on.

w.

PS—I just read the first line of the Z&T2013 paper’s abstract. It says:

Milodon, they haven’t even looked at any data. Instead, they’ve looked at computer climate model output. What they call “reanalysis data” is not data in any sense of the word. It is the output of a computer climate model which is forced in part with solar data.

Now, I’ve shown many times that computer model temperature outputs are simple linear transforms of their inputs. Given that fact, it is obvious that all reanalysis “data” is very likely to show a solar signal …

… and if you truly think that finding a solar signal in computer climate model output means anything about the real world, you are nowhere near as smart as I believe you to be.

I don’t think it’s “true”, but their proposed mechanism makes sense on its face. I brought it to your attention because they find the signal & try to explain it. I don’t rule out reanalysis on its face. I went to the paper they cite, Labitzke et al. (2002), & it looked OK, but I didn’t do any statistical analysis of my own.

I’m OK without an 11 year signal, since the sun’s irradiance & insolation as modulated by earth’s orbital & rotational mechanics (& other terrestrial & ET effects) are IMO clearly implicated in climatic periodicities on the scale of multiple decades, centuries, millennia, myriads, hundreds of thousands, millions, tens of millions, hundreds of millions & billions of years.

Would it be worth renewing the analysis, using cycle-length as the primary criteria?
===========
my point as well. there is no 11 year solar cycle. that is simply a mathematical average.

look for correlation between rate of warming and inverse length of cycle before claiming it doesn’t exist. Otherwise you are simply sailing south from England, claiming the America’s don’t exist, simply because you didn’t find it.

Theodore White says:
June 7, 2014 at 3:19 pm
“The Sun is about to enter a hibernation phase into solar cycle #25 and that will usher in global cooling, which I have been forecasting for years is coming. It is closer now and will begin officially in December 2017.”

I tend to agree with your comment and have been going naked on hurricane insurance in SW Florida since 2009, on the bet the Sun is going into a quiet period, saving a ton of money.

I alluded to this question in a previous solar thread and suggested the Earth acts like a battery, recharging and discharging its absorbed energy. Such a perfect equilibrium from one solar cycle to the next has been enjoyed without noticeable affects, except for current solar cycle 24 in modern times.

Instead of folding, try compressing and stretching like a Slinky the records to correlate each recharge discharge cycle from solar cycle to cycle. Depending on amplitude and duration of any given solar cycle our planet will have a net gain, net neutral, or net loss of energy from one cycle to the next. Maybe my comment yesterday on time lag effects of solar influence nudged Willis Eschenbach also to post an excellent discussion on this matter. I enjoy these discussions. Perhaps an understanding of battery technology recharge discharge cycles, can add to our understanding of how the whole climate of the planet works.

I’m OK without an 11 year signal
=========
So am I, because there is no 11 year solar cycle. The 11 year cycle is a mathematical average of the true solar cycle. It is an imaginary construct. It does not exist. Read Briggs about using averages for anything. It is garbage in, garbage out.

Willis, you say “However, none of that matters. Why not? Well, because the claimed effect disappears when we use the full SST and sunspot datasets.“. But then you say “This highlights a huge recurring problem with analyzing natural datasets and looking for regular cycles. Regular cycles which are apparently real appear, last for a half century or even a century, and then disappear for a century …“.

Given that Earth’s climate system is complex, coupled and non-linear [as per the latter part of your statement above], and given that any effect of the “11-year” solar cycle must if it exists be weak and/or inconsistent because otherwise it would have been quite easily found already, it follows that your argument based on it not being visible in the full SST record is invalid. ie, if it does exist then the full SST record is not the place to look.

So where should one look? Other commenters have suggestions, which I haven’t yet checked, but I would suggest looking in selected regional data. Maybe rainfall or diurnal temperature range or whatever data does exist, in various individual locations. Maybe some commenters here have already found something valuable – I confess I haven’t followed them all up.

NB. I’m not saying that the sunspot signal exists, just that if you are serious about looking for it then instead of looking in the places (eg. full SST) where you have already failed to find it, it would be a good idea to start looking in other places. Please understand that this is not meant as a criticism but as a helpful comment.

Greg Goodman says:
June 7, 2014 at 8:09 am
——————————–
“I suspect this may be correct but I’ve yet to see the “proof”.”

Greg, happy to oblige –

The best way to understand is to build and run the experiment for yourself. Start with 40C water samples under the strong and weak LWIR sources. You should notice little or no divergence in the cooling rate of the water samples. Now repeat the experiment but float a square of LDPE film (cling wrap) onto the surface of each sample. This allows conductive and radiative cooling but prevents evaporation. You should now record a distinct divergence in the cooling rate of the samples.

I have run a number of these type of experiments since 2011. I can assure you that LWIR does not effect liquid water that is free to evaporatively cool in the same manner as it would a “near blackbody”

Posts by others who have also taken in situ IR measurements of the ocean also support these results – Genghis says: June 7, 2014 at 10:22 am

The Earth rotating on its axis at about 1000 MPH while orbiting the Sun at around 67,000 MPH and circling the center of our Milky Way galaxy at about 514,000 MPH while taking about 230 million years to make one complete orbit around the center, is it any wonder why the climate of our planet is so complex. It’s enough to make your head spin. But factoring all this out, our Sun is still the main driver of Climate Change.

divorced layer of spray and spume in these (GP: avg over the oceans) conditions would almost fully absorb DWLWIR and as it does so, it would heat and would be carried upwards in the atmosphere initally warming the atmosphere and keeping the DWLWIR away from the ocean below.”

Well now, this is a fine piece of thinking. The very multiplier of heating used by CAGW proponents (water vapor) that is going to destroy the planet actually serves as a ”sunscreen” over the oceans (and very likely over the humid tropics). The mysterious 31C maximum SST temp would seem to be explainable with this effect. The hotter it gets, the more sunscreen that’s added until a maximum SST is reached.

You need to describe the natural processes that create the charts you’ve created mathematically. All you’ve presented so far is a weak correlation and correlation is worth only the paper it is printed on. I find your argument to be weak and you to be annoying. The old “See? It lines up” argument isn’t going to do it. Were I you I’d start looking closely at chronobiology as a first step.

Let me emphasize something I said earlier that seems not to have caught on. If the solar effect drives dT/dt (that is, T moves with the integral of sunspots or whatever) then the response may be WAY too slow to see any kind of 1 year pattern. What you may see is a dip in T if we get a few weak solar cycles in a row.

Testing this would require good data going back pretty far. Willis’s figure 1 isn’t quite long enough, though it shows one slightly weak set around 1880-1930. The current cycle may end up weaker than any of those. The test is trivial though — you could do it in Excel. The solution to the differential equation is just an exponential smoothing function (assuming a liner relation between heat outflow and T — which holds over the range of T we’re dealing with).

However, let me add another twist — if solar cycles drive dT/dt then dT/dt might be a function of the INVERSE of the solar driver (especially if it’s a magnetic effect, not direct solar energy). This would amplify the distinction between a weak solar cycle and a very weak one (or one with a long minimum — like we just had). Side note — this might explain why solar cycle length seems to matter. However, this would work better if solar cycles were measured peak-to-peak instead of minimum-to-minimum — which tends to split the long minimums in two.

One last twist — if we let dT/dt move by the inverse of some solar driver that driver can’t be something that goes to zero when sunspots cease; it’s more likely something like the F10.7 flux. Unfortunately, data on that is far less extensive. Still, some kind of proxy function could be built to approximate F10.7 flux as a function of sunspots.

Willis is right to want people to do more than just chatter about their hypotheses (which I have just done, AGAIN). I’m ridiculously busy but the analysis isn’t hard. If anyone thinks they can get adequate data going back a few centuries, I’d be happy to collaborate.

It would stand to reason that the weaker the solar driver be, the stronger and more visible the amplifier must be. So even on long time scales, the amplifier should be visible if it is driving trends. Therefore the amplifier should be easily located and will correlate to a very weak solar signal.

Think about this: The weaker your solar signals are, the harder it will be to prove your solar hypothesis without a really big and easy to find amplifier. And if you espouse a series of weak amplifiers I fear we are being asked to find nothing more than dust fairies, and invisible ones at that.

Day versus night temperatures vary up to 30 to as high as 40C on earth. That means that the surface temperature of the earth changes, let’s say 30C, in 24 hours… all the time. +/- 1C. Night is cold. Day is warm. We have strong evidence of the gain and time constant of the sun’s radiative effect on earth. That is all as a result of the sun being on or off so to speak.

The so-called solar constant varies 0.2% over an 11 year cycle.

So called global warming yields a signal of 0.1 degree per year… ish so they say.

It is obvious to me that the sun has a direct effect on the earth. It is also obvious to me that variations in the sun radiance must therefore effect the earth.

Trouble is, the earth spins and there are clouds. The spinning earth is pretty constant. Seems to me that perturbations in clouds and the solar radiance may yield an effect but really, how can you see that effect when it is lost in the noise created by the day-night cycles not to mention the seasonal cycle?

What is the earths daily average temperature on the surface and what is the +/- error associated with that calculation? Anyone?

It is the sun stupid. However, measurement systems are still far to primitive to resolve the signals to any degree of precision sufficient to separate periodic radiance variations from simple day to day variations.

Pamela Gray says:
June 7, 2014 at 6:07 pm
———————————-
“It would stand to reason that the weaker the solar driver be, the stronger and more visible the amplifier must be.”

This is not necessarily so. We are only looking for 0.8C in 150 years. A process of accumulation not amplification can cause this.

It is the higher solar radiation frequencies that vary most between solar cycles. It is these frequencies that penetrate deepest into the oceans. (UV-A still having the power of 10 m/2 at 50m depth.) Penetration exceeds the diurnal overturning layer, so energy can accumulate.

However we do not have sufficiently accurate SST records for 150 years to quantify the real world effect, but the mechanism is easily demonstrated by empirical experiment.

If you falsely treat the oceans as a “near blackbody” not a selective surface and only look at total TSI variation, not individual frequencies then you will end up wasting time looking for instantaneous SST response to 0.1% TSI variation, which as I indicated at the start of the thread, is a dead end.

Fair point, but no. You’ve used “at least 30%” default option. I should have stated in the description somewhere that I used “at least 2%” coverage, to get a fuller set. I also have a script which fills minor breaks.

So, fair pick, thanks for pointing it out. But no, the data I’m processing is not a broken mess.

My friend, if you think a dataset which gives you a global average from only 2% of the global data is not a “broken mess”, then you should get out more … even the 30% limit is pretty low in my opinion, that means you lack almost three-quarters of the data … very, very sketchy.

And even if that were not true, you still only get 163 years of “data” using your 2% solution … and from that you are diagnosing a 170-year signal. Does the name “Nyquist” ring a bell?

Antarctica is always frozen all year round because the Southern Hemisphere is furthest away from the Sun during its winter solstice, as opposed to the Artic which is closet to the Sun during its winter solstice and accumulating a limited amount of ice during the Earth’s yearly orbit. If this correct TSI has a distinct measurable effect on one pole versus the other. Unless the Sun has virtually no effect whatsoever because it’s a benign star.

Paul, according to my measurements, day vs night, cloudy or clear, surface ocean temperature measurements in the sub and tropical oceans do not vary at all.

If the radiative difference of over 7000 watts in a 24 hour period can’t produce a temperature change, what difference can a fraction of a watt make?

Genghis, while I think it’s great that you take your own measurements, before you start expounding you should look at other folks’ measurements as well.

In particular you need to familiarize yourself with the NDBC, the National Data Buoy Center. There are data buoys all around the US coasts that measure water temperature on an hourly basis. For example, here’s the last five days of sea surface temperatures from the ocean off of Lake Worth, Florida, which is the nearest NDBC site to Bermuda which has water temperatures:

Note that it has varied up to 2°F (1.1°C) day to night, and 4°F 2.2°C) over the five-day period …

I don’t want to discourage you, Genghis, because you’ve actually taken the trouble to take measurements and think about what they mean. As both an amateur scientist and a blue-water sailor myself, I approve heartily of that kind of an initiative.

However, your claim that day vs night the ocean surface temperatures don’t vary at all? In your place, I’d give that one a pass until I’d studied a few (dozen) buoy records …

The notion that cross-correlation between noisy time-series needs to be “adjusted for autocorrelation” and has confidence limits independent of noise-level is straight out of Lewis Carroll.

Thanks, sky. Since you have quoted “adjusted for autocorrelation” but are the first person in this thread to actually use the term, it’s not clear whose statement you are referring to. However, it certainly looks like you are trying to pass your quote off as my words, when they are your words and yours alone.

I put my request into bold caps at the end of the post specifically for poor readers like yourself, where I said

If you disagree with someone, myself included, please QUOTE THE EXACT WORDS YOU DISAGREE WITH.

Was I not clear enough for you?

As for whether the significance of the correlation between two datasets needs to be adjusted for autocorrelation, of course it does. A given correlation X between two highly autocorrelated datasets is not as statistically significant as finding the exact same correlation X between two datasets which are not autocorrelated, as a simple Monte Carlo analysis of red noise will show you very clearly … but then you knew that. Or I sure hope you did, given your claims of profound knowledge of the subject. And of course, the same is true of cross-correlations, since they are just correlations of subsets of the data.

“However, none of that matters. Why not? Well, because the claimed effect disappears when we use the full SST and sunspot datasets.“. But then you say “This highlights a huge recurring problem with analyzing natural datasets and looking for regular cycles. Regular cycles which are apparently real appear, last for a half century or even a century, and then disappear for a century …“.

Given that Earth’s climate system is complex, coupled and non-linear [as per the latter part of your statement above], and given that any effect of the “11-year” solar cycle must if it exists be weak and/or inconsistent because otherwise it would have been quite easily found already, it follows that your argument based on it not being visible in the full SST record is invalid. ie, if it does exist then the full SST record is not the place to look.

Say what? If I’m trying to find a weak signal, the very best thing that I can have is more data. For example, I can use the “folding” technique to average out the random noise.

So where should one look? Other commenters have suggestions, which I haven’t yet checked, but I would suggest looking in selected regional data. Maybe rainfall or diurnal temperature range or whatever data does exist, in various individual locations. Maybe some commenters here have already found something valuable – I confess I haven’t followed them all up.

NB. I’m not saying that the sunspot signal exists, just that if you are serious about looking for it then instead of looking in the places (eg. full SST) where you have already failed to find it, it would be a good idea to start looking in other places. Please understand that this is not meant as a criticism but as a helpful comment.

Thanks, Mike. I’ve done that. I’ve looked at water flow on the Nile. I’ve looked at rainfall in England. I’ve looked at diurnal temperature range in the Armagh Observatory data. I’ve searched for the signal in the CET.

So I fear that waving your hands and suggesting that I look in some unspecified elsewhere is all that much help …

Finally, you say:

… if you are serious about looking for [the ~11-year signal] then instead of looking in the places (eg. full SST) where you have already failed to find it, it would be a good idea to …

I don’t understand this. I’ve never in my life until this post looked for a sunspot cycle in the HadISST data. I looked where I hadn’t looked before, and I didn’t find it … so how am I “looking in the places where [I] have already failed to find it”? That doesn’t make sense. I had to look there to not find it.

Antarctica is always frozen all year round because the Southern Hemisphere is furthest away from the Sun during its winter solstice, as opposed to the Artic which is closet to the Sun during its winter solstice and accumulating a limited amount of ice during the Earth’s yearly orbit. If this correct TSI has a distinct measurable effect on one pole versus the other. Unless the Sun has virtually no effect whatsoever because it’s a benign star.

First, while the difference in instantaneous insolation between perihelion (Earth nearest the sun) and aphelion is about 22 W/m2 (on a 24/7 global average basis), there is no difference in the amount of energy delivered over the course of a year. This is because when the earth is nearest the sun it is also moving the fastest. So it ends up spending less time where it is warmer, and more time where it is cooler. And following the rules of the cosmic joke we call the laws of physics, these two effects offset each other exactly.

So while you’d think that the southern hemisphere would get more total watts than the northern because the southern hemisphere faces the sun at perihelion, in fact both hemispheres receive the identical amount.

(A related question I asked before on another thread and no one answered correctly: Which spot or line on earth receives the most hours of sunlight every year? But I digress …)

The real difference between the North and South Poles is what is below the surface. In another cosmic joke, no matter where you wander on the Arctic ice pack, no matter how cold the Arctic winds might howl, there are (relatively) warm temperatures and liquid flowing water within a few metres of where you are standing. The Arctic is only cold on the skin. Underneath that frigid ice is a layer of warm water that is constantly being replenished from the lower latitudes.

Around the South Pole, however, there’s no liquid water within a thousand miles. Instead, there are thousands of metres of ice below you, below that is frozen rock, and the freakin’ Antarctic cold goes all the way to the bone, the coldest place on earth.

So in fact, the difference in the temperature of the two poles is down to the historical accident that at present, we have a continent at one pole and an ocean at the other pole. Hasn’t always been that way, won’t always be that way, but for now, it’s what we have.

I asked “What is the earths daily average temperature on the surface and what is the +/- error associated with that calculation? Anyone?”

And you kindly responded. Thanks.

I was thinking of surface air temp not water below the surface even if it is 0.01mm. I wasn’t specific. My apologies.

Evaporation is the dominant energy sink in the energy budget. Agreed. The evaporant, water, does also change in temp as well transposition in phase. When I hear about global warming, I think about the air temperature increasing. The 0.1C delta I referred to was the surface air temp increase.

But more to my point, what is the ERROR or uncertainty in the so-called average air temperature of the earth?

you are on the right track, but left out a few other very, very important differences between the total Antarctic ice and the total Arctic ice. Let us skip Greenland for a bit.

The Arctic sea ice is surrounded by what is essentially tundra – wet, muddy, flat LAND at a rough circle at about latitude 70-72 south. In the arctic summer, the land has no ice on it at all. The Arctic sea ice drops from a March-April high of about 14 Mkm^2 to a September low of 6-7 Mkm^2 supposedly based on the 1970 data, down towards today’s average 4 Mkm^2 sea ice extents. Sea ice extents have twice gone to right at 3 Mkm^2 in 2007 and 2012.

At the earth’s radius, assuming a beanie cap over the pole – which is almost right.,
1 Mkm^2 of sea ice covers the north pole down to 85 degrees.
2 Mkm^2 of sea ice covers the north pole to 83 degrees.
3 Mkm^2 of sea ice covers the north pole down to 81 degrees.
4 Mkm^2 covers the pole down to 80 north latitude.

Thus, at today’s minimum sea ice extents in mid-September, the NOONDAY sun is only 8 – 10 degrees above the horizon! It is trying to penetrate an air mass between 34 and 16 atmospheres thick, to hit a piece of ocean whose solar elevation angle has an effective albedo on open water and average wind speeds of only 0.20 to 0.34.

The Antarctic sea ice extents surrounds the 14 Mkm^2 continental land mass + the 3.5 Mkm^2 permanent shelf ice. The minimum Antarctic sea ice extents of 3 – 4 Mkm62 surrounds that 17.5 Mkm^2, so even at its LOWEST sea ice extents, the MINIMUM effective Antarctic sea ice represents an area not of 3 – 4 Mkm^2, but 21 to 22 Mkm^2. At its MINIMUM Antarctic sea ice extents in February-March, the edge of the Antarctic sea is is not at 83 or 85 south latitude, but at 70 south latitude! At its sea ice extents maximum – now setting new records the past few years at 19.5 Mkm^2 – the total Antarctic ice cap goes fro the south pole all the way up to latitude 59 south.

And it is expanding steadily even further fro the south pole every year, every month. May 8 this year? Just that 1.6 Mkm^2 “excess” Antarctic sea ice “excess” was 97% the size of Greenland. Not as thick of course, but even closer to the equator than Greenland’s ice.

Worse, the Arctic sea ice has roughly 50% “old ice” each year, and that dirty old ice has a very low albedo measured by Curry in the SHEBA ice camps as low as 0.38 – 0.40. Average minimum sea ice albedo in the Arctic i June and July each year is not a pristine 0.93 or 0.90, but only 0.45. That Antarctic sea ice IS however almost all fresh frozen sea ice with very, very little dirt and carbon black on it ever.

Thus, the edge of the Antarctic sea ice is not only cleaner and is reflecting from a solar elevation angle 3 – 5 time higher than the Arctic sun, it is receiving five times as much net solar radiation at sea level on those same days in late August and mid-September.

Net? the Antarctic sea ice edge receives more sunlight seven months of the year, the little bit of Arctic sea ice receives more sunlight only 5 months of the year.

Thanks for the response Willis. I’m aware there is land mass below the Antarctic ice sheet as opposed to water under the Artic ice sheet. My point is the un-even solar heating of the polar regions due to the distance from the Sun during their respective winter seasons. Although it’s only a difference of 3 million miles respectively, over billions of years it makes a big difference.

Greg Goodman says:
June 7, 2014 at 8:09 am
——————————–
“I suspect this may be correct but I’ve yet to see the “proof”.”

Greg, happy to oblige –

The best way to understand is to build and run the experiment for yourself……

I have run a number of these type of experiments since 2011. I can assure you that…..

=============

” I can assure you that…..” , LOL.
Thanks Konrad. So the “proof” you claim, as I suspected, does not exist. Thanks for the clarification.

I still _suspect_ there may be some truth in the idea, which is why I’d like to see some proof. What I don’t understand is why after 30y of intense investment in research no one in climatology seems to have tested this most basic physical question, physically.

You said, above: “Why would the climate respond to a weak 22-year signal but not to a strong 11-year signal?”

Maybe “strength” and “Weakness” are habitual conventions/perceptions being applied? (How do I say this?) Can we rule in that the ‘strong’ is in fact strong, and the ‘weak’ is in fact weak? We could easily have evolved to call what we know as “Cats” Dogs, and vice-versa.

Could the Strength lie in period/duration rather than amplitude? Could there be resonance characteristics?

I suppose there’s just not enough data, and when there is enough data a lot of currently popular notions will seem quaintly absurd.

(…) My point is the un-even solar heating of the polar regions due to the distance from the Sun during their respective winter seasons. Although it’s only a difference of 3 million miles respectively, over billions of years it makes a big difference.

Over billions of years the Sun has brightened, the planetary orbits have shifted while the asteroid belts and even moons formed and grew, we might even have had a rogue planet or two pass through the system, or be captured, or collide.

Then there’s that stupid continental drift, where our current polar regions aren’t the ones we had before, it was ocean at both ends.

Projecting back the possible effects of our current Sol-Terra distance variations to billions of years ago, or just millions, might no be that wise, guy.

W: “And even if that were not true, you still only get 163 years of “data” using your 2% solution … and from that you are diagnosing a 170-year signal. Does the name “Nyquist” ring a bell?”

Nyquist says you need at least two samples per cycle. How many monthly samples do you think there are in 163 years? This has nothing to do with Nyquist.

Willis: “I had quoted your exact words about the 170-year cycle, immediately above my comment
in the same post. You had said, and I had quoted:”

You quoted my words , then ignored them and reinterpreted. Now you re-quote me and chop out even more of what I said in an attempt to refute my objection. At least try to be honest, rather that twisting and misquoting.

Nowhere did I say there was a 170 “cycle” , in fact I explicitly drew attention to the length of the data in relation to that peak and explicitly said there was no grounds to interpret it as being cyclically repetitive.

For the third time, here is what you are choosing to ignore in order to criticise me for you misinterpretation of what you think I said:

“It should also be noted that is a clear anti-correlation with period of about 140 years and a lag of half that. That is a period of time, there is not enough data to suggest this is periodic as in cyclically repetitive.”

Now please stop pointless arguments based on misquoting and misinterpreting and get back to looking at the data.

kadaka (KD Knoebel) says:
June 7, 2014 at 10:34 pm
“Projecting back the possible effects of our current Sol-Terra distance variations to billions of years ago, or just millions, might no be that wise, guy.”

With all that going on, how can anyone think there should be no significant climate change except what man makes?

You chose to use cross-correlation of hadISST in attacking Shaviv 2008, so I’ll repeat the question you forgot to answer relating to the key point you are trying to make in the article:

Without worrying about the FT of CC for the moment. What information do you think can be derived from cross-correlation. You used it to support your impression that there is no solar signal, so you must expect that something could be there that was not.

I’m guessing you were looking for a peak that is above your 0.2 threshold (but I don’t want to puts words into your mouth, so please correct me if that’s wrong).

Greg, where’s the source code? You can mess up the data so badly in so many ways (like decimating with a boxcar average filter like in the original article). We need to review the source code. (I note that your periodiogram of SSN matches mine though, so it’s probably right, or at least as wrong as mine).

My basic checklist for any signal analysis.

(1) was decimation done? Why did you bother introducing more error by decimation? FFTs on modern computers are very fast and there’s not enough reliable monthly history of any meteorological data set to make a modern computer even hiccup. If you have data sets with mismatched periods the proper procedure is to interpolate, not decimate. As a rule of thumb I always interpolate by 4x or 8x just to avoid any computation-induced issues. If you need to decimate please use a proper filter, not a boxcar average. If you want to decimate for display purposes make it the very last operation.

(2) was proper windowing done? You’ll spread sinx/x noise all over your FFT if you don’t.

(3) was the data zero-padded for better inspection of low frequency components? Note that you need about 8 cycles to get any decent quality low frequency information – the error is 1/period, and 1/8 means 12.5% error. For the ~300 years of sunspot numbers any supposed cycle any period discovered that’s > 40 years is highly suspect. It’s probably just noise. It’s not correlatable to anything else.

(4) was the data filtered before FFT to avoid aliasing?

(5) was any filtering done with a reasonable filter? e.g. a hamming filter or other linear-phase filter. averaging is wrong wrong wrong, and a clear sign you don’t know what you’re doing.

The original article that was reviewed failed every item in this checklist.

A reader on my infrequently updated blog pointed me out to your post here. Here are my thoughts about it.

Thanks, Nir, good to hear from you.

You write: “… however, I’m not seeing it. So where can we find this mystery ~11-year cycle?”. So, let me start by referencing earlier work detecting the 11-year solar cycle in the land or sea surface temperature records. The following examples (and quite a few more I didn’t mention) indicate that the peak to peak variations of either SST or land surface temperature are around 0.08 to 0.1°C between solar minimum and solar maximum:

– Douglass and Clader (2002) found a pear to peak variaton of 0.11 ± 0.02K

Thanks. Have you been able to replicate their results? I’ve looked at some of those, and have either not been able to replicate their results, or have seen their results disappear (as happens with your SST claims) when longer datasets are used.

Given these previous detections it is thus worthwhile to ask whether you expect to see a statistically significant signal in the dataset you used. i.e., you should place an upper limit and then compare to previous determinations of about 0.1°C. If the upper limit is bellow this value, then you have something interesting to say, otherwise, the result is isn’t interesting, it just means that the dataset you used doesn’t prove or disprove anything.

When inspecting your fig. 6, the signal you see there does in fact have the right amplitude and phase. So the question is not whether you see a signal or not, it should be whether the signal you see is statistically significant.

To answer this question you have to do some minimal statistical analysis which you haven’t done. For example, you should estimate the probability that the null hypothesis would give the blue line apparent in fig. 6, namely that a large fraction of the points would be as far as they are from the null signal. (e.g., by calculating the chi 2 and the effective number of degrees of freedom). I did this and found that the null hypothesis can be ruled out at better than the 99% confidence level. (and it doesn’t matter if I plotted 1-sigma or 2-sigma error bars on the points).

Nir, I agree that the result in Figure 6 shows about the right variation of the means of the folded cycles to fit your theory. As a result, you should be most suspicious of it, that kind of result is the most dangerous.

In any case, I also showed the 95%CI on those means. Given that the 95%CI barely crosses the zero point, I say that the means are not significantly different from zero. Do you agree, and if not, why?

And since the means are not significantly different from zero, I don’t see how you can claim that a cycle exists, unless it is a cycle of zeros.

You say that the appropriate statistical test is the “probability that the null hypothesis would give the blue line apparent in fig. 6, namely that a large fraction of the points would be as far as they are from the null signal.”

I can agree with that as far as it goes … but what is the “null hypothesis”? Red noise? If so, what kind? I don’t think you can do it with just a chi-squared test, that assumes white noise.

And you speak of an adjustment for autocorrelation using “the effective number of degrees of freedom”. However, all that adjustment can do is make the results less statistically significant … and they are already not statistically significant even without that adjustment.

The two possible answers that you could get are that a) the null hypothesis can be ruled out, implying that this dataset supports the idea that you see a solar signal. b) the null hypothesis cannot be ruled out, which would then just mean that this dataset does not support the idea, but it doesn’t rule it out either. Now, given that there are other data sets that clearly show the solar signal, I couldn’t care less what the answer is.

Mmmm … were I in your shoes, I don’t think I’d advertise that attitude. Sounds a whole lot like “My mind is made up, don’t bother me with facts” …

I agree that my result shows only, and can only show, that the HadISST dataset does not support the claim of a solar signal. And since you have claimed that the HadISST dataset does support the idea of a solar signal, I’m sure that you would LIKE to “care less what the answer is” from my analysis … but I doubt if that is actually the case, you’re more of a scientist than that.

Next, you don’t see a signal when using the SST from 1870. However, your conclusions don’t consider the following points:

a) The SST over long time scales is very poor, not much signal and a lot of noise. How much of the southern pacific was sampled by boats in the first 50 years of that time interval? In fact, even today it is poorly sampled.

Your problem is my data is spotty, and your own analysis used ocean heat content data?? That’s special pleading.

First, the SST data from 1880 is entirely comparable, and arguably better, than the OHC data from 1955 that you used without demur. Hundreds and thousands of ships have been measuring SST for every oceanographic expedition ship measuring OHC. In addition, for OHC you need to measure in three dimensions, meaning you need more measurements and your results have greater uncertainty.

The other reason I say that your claim is special pleading is that if the full dataset supported your claim, you’d use it without compunction or question. How do I know that? Because it’s what I or anyone else would do with a full dataset that supports our claims. Live by the sword, die by the sword.

b) You assume that the solar cycle is strictly periodic (say 11 years?) but in fact it isn’t. As a consequence, any analysis such as Fourier transforming, or folding over the period is smeared out from the non-constant period. (You have to fold the data while keeping the right phase within the solar cycle, which I don’t think you did).

Not true at all. Both folded analysis and Fourier transforms clearly show the various ~11-year peaks of the solar cycle or their averages, and the solar cycle is definitely not regularly periodic. Claiming that a similar signal won’t show up in the same analysis of the climate data won’t wash. If that were true, we wouldn’t see it in the solar data either.

In addition, I don’t “assume that the solar cycle is strictly periodic”. I’ve never said anything like that. Please provide a quotation AS I REQUESTED to support that erroneous view of what I think.

Finally, I folded the data at the solar minima and plotted their values and their average from the solar minimum forwards for 11 years because you folded the data and looked forwards 11 years.

c) Like with the previous case, you don’t estimate what is the statistical significance of the null result you find. Are the errors so large that the expected signal drowns in noise?

Look, Nir, you claimed that a signal was there. I’m not finding it. Let’s worry about the reasons and the details later.

Last, my biggest grievance about your criticism is that the analysis in the paper you considered was of 3 datasets. You chose only the SST, and disregarded the tide gauge records which shows the solar signal with a very high statistical significance. See http://www.sciencebits.com/calorimeter .Its problem is that it may have some “contaminant” from variations in the amount of ice, but that doesn’t matter if you want to prove that you see a climatic signal over the solar cycle.

One thing at a time, my friend. I try to keep my analyses focussed on one topic. As to your tidal records, you have not archived your data and your source (Douglas) is paywalled, so I am unable to replicate your results. Post up the 24 tidal datasets you used and I’m happy to take a look.

However, I’ve looked for the solar cycle in a lot of tidal records, and found nothing. See e.g.

Given that, I’m not sanguine about your sea level results, but I’ll take a look at them if you post the data.

You could also ask whether 0.1°C is large or small, and the answer is that because of the large heat capacity of the oceans it corresponds to about 1W/m^2 variations (when averaged over Earth’s surface).

So in fact, the small temperature variations observed are at leaf 6 times larger than what the IPCC is willing to admit.

Since your claimed cyclical variations in SST disappear as soon as we use a longer dataset, and since the folded means are not significantly different from zero whether we use the shortened or the full dataset, and since the cross-correlation doesn’t show a significant correlation at any lag whether we use the full or shortened dataset … I fear your quantitative analysis is wildly premature. First you need to show that a solar signal in the SST actually exists.

All the best, and again, my thanks for being one of the few scientists who is willing to come here and to defend your work against the “slings and arrows of outrageous fortune”.

w.

PS—This is far from my first venture into these sun-spotted waters. In addition to the above citations to sea level studies, see also:

Basic signal analysis – based hypothesis: If the oceans are a low pass filter, it’s entirely possible that the sun affects ocean temperature, but you won’t see it in an 11 year cycle because that signal is filtered away. Alas, we don’t have enough data to see cycles much longer than 35-40 years. Time to stop looking for patterns in the noise…

These guys have this theory. And some like greg actually know a couple bits of math.
And you make a simple challenge.
1. show me the data set
2. show me the method

that would prove their case.

Of course, they point you at papers ( not data) and they blather on about methods ( usually with no code)
and they have all manner of wild goose chases to send YOU on to prove THEIR idea.
And when you fail to prove their idea, they blather on some more about how you should have done it
and where the effect might lie.

The Sun is about to enter a hibernation phase into solar cycle #25 and that will usher in global cooling, which I have been forecasting for years is coming. It is closer now and will begin officially in December 2017.

As for ENSO, it is driven, like all climate change on Earth, by the Sun and planetary angular relationships to the Sun and Earth.

However, there will be no ENSO this year, next year, or the year after that.

The next ENSO will be a powerful La Nina, … [blah blah blah for uncountable words]

Theodore, in the head post I said:

Subject: This post is about the quest for the 11-year solar cycle. It is not about your pet theory about 19.8 year Jupiter/Saturn synoptic cycles. If you wish to write about them, this is not the place. Take it to Tallbloke’s Talkshop, they enjoy discussing those kinds of cycles. Here, I’m looking for the 11-year sunspot cycles in weather data, so let me ask you kindly to restrict your comments to subjects involving those cycles.

I was totally serious. Let me repeat that, both for you and for others. This post is not the place for your pet theory, whether it’s about the ENSO or whatever other random subject you might light on, particularly when expounded in such excruciatingly boring detail as yours was. Please take it elsewhere.

Basic signal analysis – based hypothesis: If the oceans are a low pass filter, it’s entirely possible that the sun affects ocean temperature, but you won’t see it in an 11 year cycle because that signal is filtered away.

Basic signal analys – based hypothesis: if a system responds strongly to changes in input on a day-to-day basis, as the SST does, it is unlikely that it contains a strong low-pass filter …

Woodfortrees provides an audio representation of their data. When I load the audio into Audacity, and plot the spectrum using Audacity’s built in spectrum analyzer, there is always a nice spike at 11 years. Coincidence?

“if a system responds strongly to changes in input on a day-to-day basis, as the SST does,”

Okay, I’ll just change it to a multi-bandpass filter :-) Sure the surface temperature changes daily, but the depths do not, and mixing between the two is going to be slower than daily The depths likely feedback enough to stop an 11 year cycle from being seen on the surface.

Frankly any hypothesis is possible that’s not obviously wrong – we don’t have enough data…

These guys have this theory. And some like greg actually know a couple bits of math.
And you make a simple challenge.
1. show me the data set
2. show me the method

that would prove their case.

Of course, they point you at papers ( not data) and they blather on about methods ( usually with no code)
and they have all manner of wild goose chases to send YOU on to prove THEIR idea.
And when you fail to prove their idea, they blather on some more about how you should have done it
and where the effect might lie.

Thanks, Mosh. I’m glad some folks out there understand what I’m doing here. It’s a simple challenge, really. Show me the dataset and how to demonstrate the ~11-year signal in the data.

However, I think at this point I’ve heard every conceivable excuse for not being able to find the 11-year signal …

There are several people who claim to have seen a correlation between the duration of the “11 year” cycle the amplitude and the effect of warming. Shorter the length “11 year” cycles, have stronger amplitudes with greater effect on warming.

“if a system responds strongly to changes in input on a day-to-day basis, as the SST does,”

Okay, I’ll just change it to a multi-bandpass filter :-)

While the smiley is appreciated, I fear that such speculation and on-the-fly theory modification is all too typical of people in this discussion. Look, it could be so, it might be gamma rays … but I’m interested in evidence, not imagination.

Sure the surface temperature changes daily, but the depths do not, and mixing between the two is going to be slower than daily.

Not true at all. The oceans, curiously, move in opposition to the atmosphere. By that, I mean that the atmosphere overturns during the day, and it stratifies at night. The ocean is the opposite. The upper layer of the ocean is stratified during the day, and it overturns at night. The surface cools by evaporation/conduction/radiation. Of course, cool water is denser than warm, so the surface water sinks and is replaced by warmer water from below. This nightly overturning insures the continual mixing of the upper layer, which is why that layer iscalled the “mixed layer”.

The depths likely feedback enough to stop an 11 year cycle from being seen on the surface.

Frankly any hypothesis is possible that’s not obviously wrong – we don’t have enough data…

It’s absolutely possible that all of the molecules in my coffee cup happen to be moving upwards at the same time, too, so all my coffee might spontaneously jump out of my cup … but that doesn’t make it worth discussing. I’m interested in evidence, not some hypothetical possibility of the oceans acting as a multi-bandpass future. When you can show evidence that it’s actually happening, I’ll be happy to discuss it. Until then, not so much …

You’re such a trooper Willis. I hope you decide to revisit this topic again in a future post. “Using the Oceans as a Calorimeter to Quantify the Solar Radiative Forcing”, or seeing the oceans as a capacitor like device or a simple power storage battery whatever. With over 332,519,000 cubic miles of water on the planet that’s about 352,670,000,000,000,000,000 gallons, it’s not an easy task to find that 11 year solar cycle signal in all that wet water.

Perhaps this would be a good time to revisit this previous thread of yours;

Ocean Temperature And Heat Content
Posted on February 25, 2013 by Willis Eschenbach
“That tells me that it takes about a thousand zeta-joules to raise the upper ocean temperature by 1°C.”

“However, I think at this point I’ve heard every conceivable excuse for not being able to find the 11-year signal …”

I think it highlights what one gets when one starts to look at data with no real “idea” of how the system works.

Its why I ask the question “why does anyone think the 11 year cycle show show up?”

If you start with a notion that “its the sun stupid” then you cant help but continue to insist that other people are looking in the wrong way or looking at the wrong thing

If you start with a notion that ‘damn, its really complicated, theres the sun and clouds and oceans
and GHGS, and …. and its all inter connected” then you wouldnt be shocked to find out that tiny
variations in the sun had no discernable effect” you conclude.. hey this thing is more complicated than a circuit or a steam engine.

richard verney says:
June 7, 2014 at 12:49 am
//////////////////////////////////////////
Interesting. I agree that it appears that the atmosphere serves to cool the planet.

My point about wind swept spray and spume is this:
In Willis’ article ‘Radiating the Oceans’ Willis essentially cited the gross energy transfer budget, then said that equation balances, and then said if we remove DWLWIR, from such budget, the oceans would freeze, then said we know that the oceans are not frozen, QED the gross energy flow budget must be correct. That proves nothing, since the net energy flow budget also balances and it does this without DWLWIR. The fact that either or indeed both equations balances proves nothing.

So my point is, does all the claimed DWLWIR actually enter the ocean, because even if only 99% of it entered the ocean, over time (and we are taliking from the dawn of time that Earth first acquired oceans), the oceans would freeze if the gross flow energy budget governed and if only 99% of the claimed DWLWIR actually entered the oceans.

You correctly observed that some of the LWIR absorbed in the wind swept spray and spume would be re-radiated downwards towards the ocean. I agree, but of course, it is only some of the DWLWIR (re-radiation is omni-directional with say only (somewhat less than) about 50% being re-radiated in a generally downward direction). As we talk, in broad terms, about 1/3rd of the oceans are experiencing circa BF2, 1/3rd BF 4 to 5, and 1/3rd BF 6 to 8. In open ocean, it is rare to see less than BF2, unless in the doldrums. On the other hand, there will be large areas where severe storms are ravaging. Some of the wind swept spray and spume will find its way back into the ocean (along with any energy DWLWIR that it may have absorbed), but much of it won’t since it will evaporate or rise fuelling the cloudy conditions above (and with it latent heat changes will happen as phase changes take place).

So I am posturing that in the real world conditions that we see on planet Earth, not all the DWLWIR would get entrained in the oceans. Even if it is only few percent of DWLWIR that is essentially shielded from the oceans by the divorced layer of wind swept spray and spume which ravages above a not insignificant size of ocean, this has significant impacts on those who rely upon a gross flow energy budget for explaining why the oceans do not freeze.

Currents and wind play a large role in explaining ocean temperature profiles. Of course, wind also helps drives evaporation. In one of Willis’ arcticles on ARGO, he suggested that evaporation explained why the ARGO temperature data was capped at 30degC. Whilst evaporation does play a role, I suggested to him that it is not the sole reason since there are large ocean areas with higher than 30deg C temperatures commonly recorded (eg Gulf of Mexico, Red Sea, South China Sea, parts of the Indian Ocean, the ocean off the West coast of Arrica etc), I suggested that it is currents and wind which re-distribute temperatures from the equitorial and tropical seas polewards (and ocean overturning that helps distribute SWIR absorbed in the first 10 metres downward to depth) that is the main reason why the bulk ocean surface temperature is capped at 30degC.

I have seen your experiments, the results of which are extremely interesting, and could well be of signiificance. I have often thought that these experiments should be scaled up and replicated in laboratory conditions. There are plenty of large scale ship model tanks that could be used so that one has a large volume of water and a large surface area. It must be possible to rig something up so that the effect of the relevant LWIR bandwidth on water can be tested.

I have always been bemused why climate scientists do not attempt to experiment to test some of the theories upon which they rely. I don’t count computer modelling testing. That and the poor quality of data upon which they rely without questioning, and which data they frequently over stretch, is a poor testament of the quality and rigour of this particular head of science.

Greg, now that I look a bit closer, it looks like you didn’t window – suspiciously looks like sinx/x noise in there…

====

Yes, I know what you mean, that’s why I commented on it being odd. Both datasets have a long term rise, this is what causes the roughly triangular long term from in the cross-correlation, although this does reverse at the ends.

IIRC I used an extended cosine window on that plot. This generally provides better resolution but does need essentially flat data. Of course if you use something like a Kaiser-Bessel window in that which is quite heavy damping effect you are distorting the data to suppress a long term correlation that is real. Swings and roundabouts.

(To address Willis’ 2% criticism, both series are cropped at 1880, icoads is continuous with 20% cutoff in that range.)

This time plotted in frequency and amplitude scaled by 1/f as a concession red-noise arguments.

The low frequency peak has been bent down to 114y in period. Even with the 1/f scaling it is a significant part of the spectrum. Looking at the CC function the negative correlations are separated by about 140y, without being distorted.

It is interesting that the ISST “reanalysis” data seems to almost completely remove the 11.4 year peak in ICOADS but correlates much more strongly with SSN at 10.2 and 5.25 years.

I don’t know the mojo that is used in deriving ISST so I can’t say which is better, maybe ISST is resolving some detail better than unprocessed ICOADS. I am a little suspicious of the how featureless its is around the zero lag tough:

It’s absolutely possible that all of the molecules in my coffee cup happen to be moving upwards at the same time, too, so all my coffee might spontaneously jump out of my cup … but that doesn’t make it worth discussing.

For a straight-walled cup that’s 3″ deep and filled to the brim, the average kinetic energy per molecule would need be enough for a ballistic trajectory at least 1.5″ high, the average height to clear the edge.

And it would be quite a sight to see the coffee flash transform into a supercold solid powder, with much latent energy irretrievably radiated away, before crashing back down into the cup as a cooler liquid with perhaps some ice as the kinetic energy is reclaimed.

In another thought model, there would have to be enough thermal energy in the coffee to expand a (perfectly insulated) hot air balloon from room temperature to generate enough lift to suspend that mass of coffee.

I don’t see it happening. Unless you drink your coffee far hotter than I do. And far stronger too, possibly.

“Mmmm … were I in your shoes, I don’t think I’d advertise that attitude. Sounds a whole lot like “My mind is made up, don’t bother me with facts”

Willis, when studying a dataset and looking for an effect (such as a solar/climate link), you can either detect at some significance or place an upper limit at some significance. If however the upper limit is above the signal I expect, it means that the dataset is irrelevant for proving or disproving the effect, which implies that “I couldn’t care less”, caring more about it would be a waist of time. This wouldn’t be the case if the upper limit was below the signal I expect (it would make me worry), or the detection at a high statistical significance (and it would make me happy).

Tide gauge records:

It should be apparent from reading Shaviv 2008 that the tide gauge data is the dataset having the solar signal detected with the highest statistical significance, so ignoring it is kind of missing the whole point of Shaviv 2008. Namely, the tide gauges prove that the there is a solar signal and you can consistently see it in the noisier SST and even noisier heat content.

As for the tide gauge data record itself, I didn’t collect it so no point in republishing it (it is not my habit of publishing other people’s stuff, especially after having being threatened once with a lawsuit). I downloaded it from a repository available to anyone with an internet connection. The stations I used are those chosen by Holgate (so that I cannot be accused in cherry picking). The only difference (and it is clearly explained in the the paper) is that I averaged the derivatives of the stations and not differentiated the average to get the sea level change rate. This way I avoid the spurious jumps that you get in years with stations added and removed, which otherwise contributes a lot of noise (and which people didn’t realize and therefore remove before). Anyone can redo what I did.

Folding the data:

If you properly folded the data, than my apologies. I assumed you didn’t because the folded graphs have “years” and not a phase for their x-axis (which is still problematic towards the end of the cycles, besides being misleading).

Nothing more to learn from this thread. I am leaving it to allow Willis and Mosh to tongue kiss. That’s not why I come WUWT.

Meh. It’s Willis talking math, and also cycles and somehow the solar system. Thus a practical guarantee the comments will be infested by Goodman holding court, trying to impress with his madz math skillz, seeing how many he can direct to his site to be flabbergasted by the blabbergasting, as he ruthlessly attacks all perceived inferiors with neither mercy nor understandable explanations.

When Goodman sets up a meeting of his Superior Geniuses Only club and hangs out the “No Ignorant Morons” sign, I know the chances of learning anything in the Comments is vanishingly small. A quick glance through is all they’re worth, and likely that will be wasted effort.

Gary Pearse says:
June 7, 2014 at 5:32 pm
/////////////////////////////
Garry
What Konrad and I were discussing is whether DWLWIR effectively heats the oceans. I don’t want to discuss that at depth since it is not directly germane to Willis’ present article.

That said, the issue is whether the heating of the oceans is entirely by solar, or does DWLWIR play a significant role? Konrad has performed an experiment that suggests that DWLWIR does not effectively heat the oceans. I was considering the position from a different perspective, namely real world conditions that are encountered every day over the oceans.

The issue is what is going on in say the 10metre atmosphere layer above the ocean, the pico layer of the ocean, the first few micron layer of the ocean, the first few millimetre layer,and say the first 10 metres of the ocean. this raises the issue is a photon just a photon, or does the place where the photon is absorbed play a material role.

In general according to the K&T energy budget cartoon, DWLWIR ad approximately twice the energy of solar. Given the absorption charactericis of SWIR in water (and bear in mind that sea water is not pure water), Solar IR is being absorbed in a volume represented by about the first 20 metres of water (some is absorbed at depths well below that). Given the absorption characteistics of LWIR in water, DWLWIR is absorbed within 10 microns, with 60% of all DWLWIR absorbed fully within just 4 microns.

Thus in broad terms as much energy that solar imparts into a volume of 20 metres of water is through DWLWIR being absorbed in just 4 microns (ie, 60% of all DWLWIR is fully absorbed in 4 microns and DWLWIR has twice as much energy as Solar SWIR – 60% of double is aprroximately the same in broad terms),

Solar SWIR does not boil away the oceans because all the energy is absorbed within a large volume, ie. a 20 metre layer. If the absorption characterics of SWIR was different such taht 80% of it was fully absorbed within just 4 microns (I have upscaled it to bring it in line with DWLWIR – K&T energy budget cartoon suggests DWLWIR is approximately twice that of Solar IR), the oceans would have boiled away long ago.

The issue is why if DWLWIR is heating the oceans, it does not lead to rapid and copious evaporation of the ocean? Potentially, there is so much energy being absored within the first 4 microns that it would give rise to about 14 to 18 metres of rainfall annually (which of course we do not have). So this raises the question whether the DWLWIR absorbed within the first 4 micron layer can be dissipated to depth before it would cause rapid and copious evaporation. It cannot be dissipated to depth by conduction since the first 4 microns is coller than the first few millimetres of the oceans. The temperature flux profile is upwards and hence energy absorbed in the first 4 microns cannot ‘swim ‘ against that ‘tide.’ The only other process suggested is ocean overturning. Willis in one of his comments explains this diurnal event. It is a slow mechanical process which could not dissipate energy faster than the rate of absorption of DWLWIR fuelling evaporation.

There are fundamental difficulties with the interaction iof DWLWIR and the oceans. The fact that we do not see copious amounts of evaporation provides some suppport for the view that DWLWIR does not effectively heat the oceans. Konrad’s expiriment suggest that it does not. I do not challenege his expiriment, but additionally postulate that there are other processes involved in the real world conditions encountered by planet Earth that may mean that not all the DWLWIR actually finds its way into the ocean, but instead much of it remains in the atmosphere above the oceans, never entering them.

Why is this relevant? well realy as to whether one accpets the gross energy flow budget, or the net energy flow budeget at the one that best describes real world conditions encountered on planet Earth.

KDK “Thus a practical guarantee the comments will be infested by Goodman holding court, trying to impress with his madz math skillz, seeing how many he can direct to his site to be flabbergasted by the blabbergasting”

I’m not trying to redirect anyone anywhere, I use my climategrog site as pastebin because this site does not let commenters insert graphics. I used to use tinypic until they put so much crap in the way, so I starteda WordPress site where it is at least clean and legible. This also means I can add a description of how plots are derived and what the data is.

Since I seem to be almost the only one apart from Willis actually prepared to do anything other than talk and handwave, that may appear “superior” to some. Remind me of what you have ever contributed to scientific analysis here.

There are several people who claim to have seen a correlation between the duration of the “11 year” cycle the amplitude and the effect of warming. Shorter the length “11 year” cycles, have stronger amplitudes with greater effect on warming.

Thanks, John. Yes, there are stacks of people who have made all kinds of claims about finding signals from both the duration and the strength of the sunspots in the climate datasets. Unfortunately, I haven’t found one yet that withstands serious examination.

I went to take a look at the first result on your list, Christensen and Lassen. I find that they have used an 11-year running mean filter on the sunspot data, a truly destructive choice of filters. Not only is it a boxcar filter, with all that implies. The choice of an 11-year filter in a sunspot dataset with a strong signal that varies both above and below 11 years munges the data horribly. See my post called “Sunny Spots Along The Parana River” for a full discussion of the problems caused by this kind of filtering. The short version is that it turns your sunspot data into garbage, and any conclusions based on the procedure are useless.

One more study looked at … how about you guys crank up your cranial engines and actually think critically about these studies before dumping them on my plate? It just makes you look foolish when you cite junk.

In any case, what I’m looking for is not a study. It is a dataset and a corresponding procedure for extracting the 11-year cycle. I’m not the guy propounding this damn theory that there is an 11-year cycle out there in the climate data. If you want people to believe it, we need evidence. Not just pointing at yet another random theory in yet another random study. Half of all scientific studies are crap, and I’m skeptical about the other half. The fact that some claim got printed means nothing to me. Actual evidence of a dataset and a procedure that you know works is what I’m waiting for people to show me.

Thanks for the response Willis. I’m aware there is land mass below the Antarctic ice sheet as opposed to water under the Artic ice sheet. My point is the un-even solar heating of the polar regions due to the distance from the Sun during their respective winter seasons. Although it’s only a difference of 3 million miles respectively, over billions of years it makes a big difference.

You didn’t get what I said, likely my lack of clarity. The south pole receives no more or less solar energy than the north pole. Yes, the instantaneous insolation is lowest in the antarctic winter. But that is exactly compensated for by the difference in speed. The earth spends less time in the warmer zone and more time in the cooler zone. Both poles receive exactly the same amount of heat over the year. It is guaranteed by the laws of physics.

As a result, there is no “un-even solar heating of the polar regions due to the distance from the Sun”.It doesn’t exist. Both poles get the same amount of energy. The same is true of the entire southern hemisphere. It gets the same total amount of energy from the sun as does the northern hemisphere, despite the ellipticity of the orbit. Strange, but true.

Konrad:
Proof does not exist? I have been running these experiments since 2011 –

I am not lying to you. I am not a climastrologist. I work in engineering.

Just conduct the experiment as shown. Remember –

“Tell me I’ll forget. Show me I’ll understand. Let me do it I will KNOW.”

Type is cheap. Do the experiments and you will know. I have little interest in just publishing my results. I am more interested in others replicating experiments.

=====

If you work in engineering, you know that your jpg diagram of two black boxes in no way represents “proof” , so why is that all you posted? It’s nonsense.

You equally know that comments like ” I can assure you” or “I am not a climastrologist” carry ZERO weight either.

Since no one else seems to have done this and you “have little interest” in publishing any results you may ( or may not ) have, that leaves us where were with NO proof. So your earlier claim that there is proof is unfounded. I did not call that lying, That is your choice of words.

If you have something lets see it. If you don’t, stop posting false claims that proof exists when it does not.

My impression (which could obviously be mistaken) is that you failed to get any credible results or never even got as far as constructing the experiment. You hope that someone else will do the donkey work and “reproduce” what you failed to produce. You will presumably then claim credit for the “original” work that “proved” you can’t heat ventilated water with IR.

Now I’d be very happy if I am mistaken and you get motivated to publish if you have some credible results. Thus far, it’s getting a bit like Murray Salsby syndrome.

Since I seem to be almost the only one apart from Willis actually prepared to do anything other than talk and handwave, that may appear “superior” to some. Remind me of what you have ever contributed to scientific analysis here.

And that makes it official. If it ain’t Willis waxing nostalgic about atolls or fishing or building pergolas that are proofed against climate change, his work just ain’t worth reading anymore since Goodman might be there. If you don’t want to see manure excreting, stop looking at bungholes.

Willis ….“However, I think at this point I’ve heard every conceivable excuse for not being able to find the 11-year signal …”

Mosher …… I think it highlights what one gets when one starts to look at data with no real “idea” of how the system works.
__________________________________

Willis, Mosher,

What boggles my mind, is why nobody has seriously looked into this before, as a part of all the Climate slush-funds that are sloshing their way around the world. Why has nobody fully analysed all the possibilities? And why is it left to an amateur to debunk the many absurd claims that have been made (no offense, Willis, but I don’t think you receive a stipend from an educational establishment for your endeavors).

Clearly there are cycles in the climate record, with the 60-odd year cycle being perhaps the most obvious. So why do we not know what the primary driver of that cycle is? And saying it is the PDO is not an answer, for what drives the PDO?

Personally I think this cycle has to be something other than a natural resonance that just ‘happens’. Something is driving it. I feel the Sun probably is a primary influence, but how? Surely, these are the questions that Mann et all should be investigating and answering, rather than just scaring everyone with absurd extrapolations of temperature and disasters. How can the IPCC say they understand the Earth’s climate out to the next 150 years, if they do not understand the rather obvious 60-year climate cycle?

And sorry, Willis, please don’t ask me to do the math first – I would not know a Fornier Transformation from a Ferret. But surely there is someone in the IPCC or in academia that does. Or, failing that, why not just pay Willis to look. Geeez, in terms of the amount of money wasted on climate ‘science’, giving Willis a stipend and an office would be a drop in the (warming or cooling) ocean.

Willis, the buoy’s don’t measure the surface temperature, the skin where the evaporation and radiation actually takes place. They measure the water temperature just under the surface which clearly shows the night vs day pattern, just as the air temperature above the surface does too.

Do you know of a source that actually measures the surface temperature? I can also confidently predict (postdict?) that if you match the wind speeds to your buoys temperature data that the wind speed dropped allowing the temperature to rise.

Also if the wind speed isn’t high enough the surface temperature will show the night vs day pattern, if it is clear skies. Pointing the IR gun skyward, the bottom of the darker clouds is generally in the 76˚ F range while the clear sky areas is around 34˚ F at night and in the 40˚ F range during the day. (I use Fahrenheit because it is a finer scale, whether it is more accurate or not is another thing)

I am extremely familiar with the buoy data, as well as all of the weather forecast models, Chris Parker, satellite data, etc. etc. As the Admiral gets very upset if the sailing conditions are not to her liking and my number one goal in life is to keep the Admiral happy.

Willis: “In any case, what I’m looking for is not a study. It is a dataset and a corresponding procedure for extracting the 11-year cycle. ”

Since we are all agreed that the “solar cycle” is it not a single fixed period no one is going to be able to extract such thing even if there is a solar signal to be found. It has to be linked directly to some proxy of solar “activity”.

You chose cross-correlation to examine that, which seems a very sensible first step. Unfortunately you seem to have chosen annual averages. one thing for which Shaviv2008 could be criticised for. That appears to degrade the signal to a level you regard as non existent.

When I do the same thing with monthly data and find what your own criteria suggest should be significant, you steadfastly ignore the result and discuss anything else instead.

As a matter of interest, Willis, will your many analyses of the data show up a variable cycle, like the Sunspot Cycle – which appears to vary between 10 and 14 years? Does a FT of the raw SSN data, for example, show a prominent peak at 11 or so years?

So can we easily detect variable cycle-lengths in the temperature record? Is this why longer cycle lengths are more prominent in the temperature data, because the shorter cycle lengths are variable and jumbled? Is the 60-year PDO climate cycle a culmination of many variable smaller cycles of about 12 years in duration?

And please don’t ask me for the math. As mentioned previously, I would not know a Fornier Transformation from a Ferret. But I am interested in the results.

Willy
I have followed with interest your efforts to detect the 11-year solar cycle in meteorological data and the lack of evidence for any correlation. So I am wondering whether longer-term variations in solar activity have had any effect. The 14C and 10Be radioactive isotopes provide good proxies for solar activity. Their anticorrelation with the historic temperature swings of the Little Ice Age and the Medieval Warm Period may not be long enough for any solid conclusion. However, Bond et al 2001, Science 294, 2130 found good correlation over 12 000 years in cores of layered ocean sediments. In another example Neff et al 2001 Nature, 411, 290 found excellent correlation of 14C with the delta18O proxy for monsoon rainfall in a stalagmite from a cave in Oman from 6200 to 9600 years ago. I would welcome your views on such studies. Even though we do not know the mechanism, how good is the evidence for a solar contibution to climate change?

Over the record, it’s doing a FT, limited from 1 year up to 50 years. “Magnitude” is something done because that particular FT method uses complex numbers (they have real and imaginary components), there are others that don’t. “Magnitude” joins the imaginary portion with the real part so the info isn’t lost because the imaginaries aren’t being displayed.

9 and 12 years are stronger than 11 years over the full record. The 24 years makes sense because the poles flip during the 11+ year cycle, so it takes 22++ years for the full Hale cycle.

So can we easily detect variable cycle-lengths in the temperature record?

You now have a tool you can play with, with the datasets it has access to. Go forth and explore.

I read that some indigenous peoples of Alaska are losing their traditional fishing grounds because of AGW driven permafrost melting causing the shoreline to subside into the ocean.
I pulled up a satellite image of an unrelated coastline, determined from the well-accepted Beysian/Confusium correlation applied to beach gravel distributions that 1,256,342 years ago the average tidal fluctuation was 3.25 mm per parsec less than today. And, no, I did not forget to regress the dilithium cross-compounded monolith by the well established factor of wtf^2+(hoo-nos). Sheesh, anybody who knows anything knows how to do that.
Applying statistical slicing, dicing and leveling it appears highly likely that a large scrap tire dump outside Podunk, OK is the only possible source of this frightening threat to humanity and the only way to mitigate this end to life as we know it is to redistribute these tires via drone to each & every household using gender neutral genetic marker addresses.
My data, software, and methods are in a sealed and booby trapped canister at location 75.225 degrees W, 0.000 degrees N. Prove me wrong, I dare you, otherwise my work is unimpeachable.

Kakada.
(((So can we easily detect variable cycle-lengths in the temperature record?)))
You now have a tool you can play with, with the datasets it has access to. Go forth and explore.
___________________________________

Thanks, Kakada, that is most interesting. But I think I will need a Willy to explain what it means.

From what I see, the 11 year sunspot cycle is missing in this Fourier Transformation of SSNs, while the double cycle is very prominent. And this is despite the approx 11 year cycle being the most prominent when visually looking at a SSN graph. And yet the triple or quadruple cycle is missing from the Fourier. Why does a double cycle display, but not the single, triple or quadruple?

So could the sunspot cycle be visible in the climate record as a quintuple (60-year) resonant or heterodyning multiple of the smaller (approx 12-year) sunspot cycle??

Ralph “From what I see, the 11 year sunspot cycle is missing in this Fourier Transformation of SSNs, while the double cycle is very prominent.”

Sorry Ralph but WTF.org is a crock for anything more than fitting inappropriate linear trends to non-linear data and distorting it with crappy running means. It is totally inappropriate to offer FT as a click-go-tool like that. Also it is so badly present that I, having done plenty, can’t relate to the crap way the result is plotted.

FT does require quite a bit of understanding before you can make any sense of what it does. It’s well worth looking into but splashing about on WTF.org will be totally fruitless and frustrating. Don’t even try.

Willis’ “slow FT” code is available to play with and is probably more intuitive that a true FT. Look at this recent threads for code, examples and ample discussion by lots of people with varying degrees of knowledge of the subject.

You will also find answers to some of the questions you asked above, SSN contains three close frequencies around 11 years plus circa 22y. This is why it is a shape-shifter. You will find lots of detail on that.

No point in repeating the highlights here, suggest you go and read the threads. As always, more chaff than wheat so take coffee and biscuits with you. ;)

“Is the 60-year PDO climate cycle a culmination of many variable smaller cycles of about 12 years in duration?”

IMO, it is likely that the various long period climate “oscillations” are the result of interactions of lunar and solar influences with periods around 9y and 10-11y respectively. But climate is complex and the last 30y have largely been wasted attempting to prove a foregone conclusion to the exclusion of all else.

Perhaps in another ten years a better understanding of climate as more than a single variable problem will have emerged.

Once again I must be failing terribly at what WAS, I thought, the scientific method. First observe. If it cannot be observed it is likely to be such a small affect it can safely be ignored. That is the case with TSI. It does indeed have the potential to cause a cyclic change in temperature and can be mathematically calculated. But it can be readily overcome by much more powerful intrinsic variability, thus can be safely ignored, buried as it is in noise. Many here on either side of the solar debate advocate that position: ignore TSI and yes Earth has powerful intrinsic-driven variability (and think so correctly IMO). Yet, readily ignoring a mechanized plausible mathematically defensible solar related addition to Earth’s temperature while admitting to powerful intrinsic variability does not seem to inform them about ignoring other far less powerful solar parameters. The logic escapes me that a small little mutt can be ignored but lets focus in on the hair on a flea’s ass. Even more, let’s focus in on that after we have split it in two and statistically curled it.

Why does a double cycle display, but not the single, triple or quadruple?

Because the “11 year cycle” is only a half cycle. At the bottom of an 11-yr portion, the magnetic poles flip, which is actually a drawn-out thing where they might not flip simultaneously. The Sun is a messy place.

The Sun has to pass through the bottom of another 11-yr portion for the magnetic poles to return to the previous orientation.

So what you see are Hale cycles, the flipping and the return together, which shows up as a 24-yr cycle.

The “triple” would be three half-cycles. The “quadruple” would only be the second harmonic (Frequency * 2) of the Hale cycle. So neither should show up much. Although given the variations in the “11 year” length, a “triple” could be anywhere from 28 to 36 years long.

Of course, things change when you throw away some data. I’m going to start at 1880, when the GISTEMP dataset starts, and throw away the first 130 years, half the data.

Now we’re down to only five full Hale cycles, using the 24-yr amount. Before we might have had eleven, or ten. The 24-yr high peak is gone. But there are many iterations of the “11 year cycle” remaining, shown peaking at 13 but still strong at 12 and 11 years.

So could the sunspot cycle be visible in the climate record as a quintuple (60-year) resonant or heterodyning multiple of the smaller (approx 12-year) sunspot cycle??

Let’s look. We’ll do a FT of GISTEMP too. I’m using the “normalise” function to rescale both the same for easy comparison, usable as we’re just matching peaks.

Also with only 134 years of data, looking for a 60 year cycle is a stretch, I’ll bump it out up to only 65 years.

Where is that “60 year” cycle? Can you really claim there is anything between the two that really matches up, except perhaps the Hale cycle at 24 years, which might be spurious in the temperature data?

Whoops. Re previous comment, “second harmonic” was in error, that’s not frequency*2, not an overtone. Although the link is still informative reading. It’d actually be the 1/2 subharmonic, frequency divided by 2.

But you get the idea. What happens every 24 years may look like it happens every 48 years, or 72 years, etc.

mosher thinking-
“The solar variation is so minor that I do the following thought experiment.

I make a chart of TSI versus time.
I label it c02.
I make a chart of temperature versus time
I label it temperature.

Then I imagine what a skeptic would say If I claimed that chart one helped to explain chart two.”

make it about something else, then ignore the obvious things like … ohh, just about every proxy we have that says co2 lags temps, NOT the other way around AND temps move with sunspot numbers through Be records. hmm, it takes skill to ignore so much.

this thread is a fine example of that skill in action. willis congrats mosher for thinking like him, which says it all really.

it is obvious that the goal of this thread is not to determine whether there is an eleven year cycle evident in the record, it is just to belittle those looking for it. it really is not a simple task. there are just so many possibilies there. to claim that one knows without doubt that it should be most visible in the the 11 year cycle is a clear failure and outright arrogance. it is this form of arrogance that we see every day in the climate science community. it is unexpected from the engineering community, which i thought willis was from.

“We hold similar views, and recall that we engaged in much discussion on this on Willis’ article on ‘Radiating the Oceans’ (which i personally consider to be not one of Willis’ stronger articles – sorry willis, just my personal opinion). However, the point that the warmists would raise is that if DWLWIR heats the atmosphere, even if it does not heat the ocean below, then due to the warmer atmosphere above the ocean,the heat loss from the ocean is lower/slower, thereby helping to maintain or even produce higher ocean temperatures over time.”

Just complimenting you guys along with Greg on some good insights and cogent explanations. I did want to point out that the mention of spray and spume and the generally wet conditions above the ocean are only part of the LWIR absorption. Water vapour itself didn’t get a direct mention.

Something else worth mentioning is that ocean water is quite reflective as well as having a high emissivity. I do not believe the story about water having an E of 0.7 – I have measured too much water with an IR thermometer to accept such a low number. The emissivity of water is in the high ‘9’s’. A common mistake would be to measure the water’s bulk temperature and interpret that as being the same as the surface skin temperature. It is about the same as black oil which is also quite reflective though they react differently to the angle of incidence.

The big influence is of course the water vapour near the ocean. I don’t think much LW gets to the surface. What leaves bounces back and forth a lot too of course, so overall the vapour is an insulating blanket. CO2 is simply a bit player there is so little of it.

A friend sent me a message the other day saying that the volume of water condensed from vapour created per annum by burning fossil fuels is about the same as the volume of Lake Simcoe in Ontario. That’s a lot of GHG but also a lot of condensing heat transport medium. The thunderstorm thermostat hypothesis rules, OK?

“IMO, it is likely that the various long period climate “oscillations” are the result of interactions of lunar and solar influences with periods around 9y and 10-11y respectively.”

I agree. I do not see a peak in the SST PSD specifically associated with 11 years, but there are several peaks at harmonics corresponding to such interactions, and in measure of what might be expected if they modulate one another.

I don’t want to get mired again in non-productive dialog with Willis. But, I did happen upon this entry in Wikipedia which may be of interest to him. Perhaps it has already been pointed out to him, and I’ll probably get blasted for some sort of imagined deviousness in providing it. For the record, I’m not trying to make any point at all, just trying to be helpful, as always.

You chose to use cross-correlation of hadISST in attacking Shaviv 2008, so I’ll repeat the question you forgot to answer relating to the key point you are trying to make in the article:

“Forgot to answer”? I never even noticed the question. As usual, your assumed omniscience doesn’t do you credit. You don’t know what other people know or have forgotten, and your assumption that you do know is neither pleasant nor conducive to discussion.

Without worrying about the FT of CC for the moment.

Naw, let’s worry about it. You proposed the bizarre procedure of doing a fourier transform of a cross-correlation function, and made ludicrous claims about it. I showed that it reveals nothing about the target dataset, and that you’d get similar results from red noise. Now you suddenly want to forget about it … as would I in your place. But we digress …

What information do you think can be derived from cross-correlation. You used it to support your impression that there is no solar signal, so you must expect that something could be there that was not.

I’m guessing you were looking for a peak that is above your 0.2 threshold (but I don’t want to puts words into your mouth, so please correct me if that’s wrong).

Glad to. That is not “my” 0.2 threshold. It is the calculated level for statistical significance at p = 0.05, as calculated by the program, without adjustment for autocorrelation. What do you use for statistical significance of a cross-correlation, if not that?

No clue. You haven’t indicated anywhere what exact data was used to make the graph, nor how you made it. Since the significance level is a function of both the number of data points and the standard deviation of the two datasets, there’s not enough data to answer your question.

What your cited graph actually looks like, however, is that you forgot to detrend your datasets before doing the cross-correlation …

Peter, whenever like you someone starts bitching and whining about people’s choice of computer language, and starts claiming great statistical insight into the errors of others without identifying those errors, it’s a clear sign that they can’t find a problem in my computer code or logic, so they are grasping at straws … Peter, if you have a problem with my work or the work of others, either specify it or go away. This kind of boastful attack you’ve put on here is as far from science as you can get.

You’re such a trooper Willis. I hope you decide to revisit this topic again in a future post. “Using the Oceans as a Calorimeter to Quantify the Solar Radiative Forcing”, or seeing the oceans as a capacitor like device or a simple power storage battery whatever. With over 332,519,000 cubic miles of water on the planet that’s about 352,670,000,000,000,000,000 gallons, it’s not an easy task to find that 11 year solar cycle signal in all that wet water.

Thanks, Michael. We can find the daily solar signal and the annual signal with no problem at all, despite the 352,670,000,000,000,000,000 gallons of water. And people keep claiming that they can find 60-year and 100-year solar cycles in the ocean temperatures (although I’ve not seen them).

Finally, and most relevant to this post, Nir Shaviv has claimed very strongly that he can find the 11-year signal in the SST (although I can’t find a significant signal)

So it’s not clear why you think an 11-year signal would be hard to find.

Nothing more to learn from this thread. I am leaving it to allow Willis and Mosh to tongue kiss. That’s not why I come WUWT.

I love these charming folks that like to make their departure note as nasty and vicious as possible. Somehow, they are under the illusion that this makes them look … what? Look smarter? Look better? Look strong and decisive?

Mostly it just makes them look terribly needy. Why else would they care what we thought about their leaving? My goodness, what if we didn’t notice that they had left, how terrible that would be! Better to go out with a bang …

Unsurprisingly, it’s usually the anonymous interchangeable internet popups like Alex who do it, the folks that are unwilling to sign their own name and own their own words.

Fortunately, it’s a lovely morning outside, and last night the gorgeous ex-fiancee and I got to watch a couple of fox kits gamboling and playing on our porch … the world is good.

Willis, if you reply to on phrase in a comment, I am assuming telepathic powers by assuming you read to following line as well.

Greg Goodman says:
June 7, 2014 at 12:56 pm
“So smart I’d done it and posted it 9 hours before you told me “directly” to get off my butt and do it.
I’ll take that as an almost apology ;)

Without worrying about the FT of CC for the moment. What information do you think can be derived from cross-correlation. You used it to support your impression that there is no solar signal, so you must expect that something could be there that was not. ”

===
But I digress. Let’s continue the search for the illusive solar signal.

I used the full hadISST monthly data from the link shown in the graph. The SSN data was cropped to the same starting date (1870). The cross-correlation is the cross-correlation of the full overlap available at each lag ( as I’ve pointed out before a flat line is not valid, it should curve up as lag shortens the data. That’s not really an issue here though, it won’t vary noticeably in the central portion.)

W: “What your cited graph actually looks like, however, is that you forgot to detrend your datasets before doing the cross-correlation …”

The slope affects the correlation. If you want to know where the max correlation happens and what it’s value is, why would I want to distort both datasets by removing a spurious linear trend from both before doing CC?

It should be apparent from reading Shaviv 2008 that the tide gauge data is the dataset having the solar signal detected with the highest statistical significance, so ignoring it is kind of missing the whole point of Shaviv 2008. Namely, the tide gauges prove that the there is a solar signal and you can consistently see it in the noisier SST and even noisier heat content.

As for the tide gauge data record itself, I didn’t collect it so no point in republishing it (it is not my habit of publishing other people’s stuff, especially after having being threatened once with a lawsuit). I downloaded it from a repository available to anyone with an internet connection. The stations I used are those chosen by Holgate (so that I cannot be accused in cherry picking). The only difference (and it is clearly explained in the the paper) is that I averaged the derivatives of the stations and not differentiated the average to get the sea level change rate. This way I avoid the spurious jumps that you get in years with stations added and removed, which otherwise contributes a lot of noise (and which people didn’t realize and therefore remove before). Anyone can redo what I did.

Nir, sorry for the confusion, I can see my exact wording wasn’t clear. I didn’t mean to ask for the actual records. All I need are the names of the stations that you used.

However, this is a perfect example of why transparency in the form of archiving the data as used is so important in science. It appears from your comments that you’ve forgotten that you didn’t use for your analysis the 177 stations chosen by Holgate. You mentioned the Holgate data in your study, but in your actual analysis, you used the 24 stations chosen by Douglas, so that’s what I need. As I said in my request,

As to your tidal records, you have not archived your data and your source (Douglas) is paywalled, so I am unable to replicate your results.

All I need are the names of the 24 stations that were chosen by Douglas and used by you in your analysis. I can do the rest.

If you had archived your data and your code, you wouldn’t be bothered with having to go through these misunderstandings. In addition, we could examine your code and see if you’ve made any of the hundreds of common foolish mistakes that have bedeviled and tormented myself and other programmers since the invention of computers. I wrote my first computer program in 1963. It had bugs. Welcome to computing.

In addition, without your code it’s not clear what you’ve done. For example. Did you detrend the tidal records before you took the first differences? It makes a big difference, and you don’t mention it. Did you standardize the tidal records before you did the averaging? Again, it makes a big difference and you didn’t mention it.

Note that this is not a problem with your description. Your description is fine, neither better nor worse than the average for the breed. The problem is much deeper, and has nothing to do with you.

The problem is that English is far too vague and uncertain and imprecise a language to describe a series of computer operations. That’s why we don’t program in English—it is inadequate to the task. So no matter how detailed your English language description of your computer program might be, it’s not enough to answer all of the possible questions and reveal the hidden flaws.

Finally, even if you could describe precisely what it is that you think you did in the computer code, I can’t tell you how many times I’ve thought that I knew what I did in the code … and it turned out that the code was doing something completely different. So even if a scientist is perfectly candid and honest and detailed about what he thinks he did … in the real world, that means little about what the computer did when he wasn’t looking.

As a result, I fear that as it stands your study is not science at all. It is merely an advertisement for science. Science is transparent. Your work is totally opaque, and my motto is simple—no data, no code, no science. I can’t replicate your work without the data and code. I can’t identify hidden errors in your work without the data and code. I can’t find out if you detrended the tidal records without the data and code. In short, and sadly, as it stand your work is unfalsifiable because it cannot be examined.

As a result, it’s not science, it’s just an advertisement for your claims.

However, all is not lost, nothing is final. You could turn your study into real science by the simple expedient of archiving your data as used and your code as used so it can be examined for errors … as I and many other scientists do as a matter of course. It’s a pain, I hate cleaning up my code and putting the data in a useable form, but it only has to be done once for each study. Of course, I’m archiving data and code at the rate of a couple of studies a week, so archiving is more of an ongoing struggle for me than you, but hey … that’s science.

In any case, archive or not, it’s your choice. And in the meantime, a list of the 24 tidal stations that you used in your analysis would be much appreciated.

All the best, and again my thanks for you coming to defend your work, that’s science at its finest … or it would be if we could actually examine your work.

“Mmmm … were I in your shoes, I don’t think I’d advertise that attitude. Sounds a whole lot like “My mind is made up, don’t bother me with facts”

Willis, when studying a dataset and looking for an effect (such as a solar/climate link), you can either detect at some significance or place an upper limit at some significance. If however the upper limit is above the signal I expect, it means that the dataset is irrelevant for proving or disproving the effect, which implies that “I couldn’t care less”, caring more about it would be a waist of time. This wouldn’t be the case if the upper limit was below the signal I expect (it would make me worry), or the detection at a high statistical significance (and it would make me happy).

Nir, thanks for the reply. However, I fail to see how that applies here. YOU claimed that the HadISST dataset was relevant for showing a significant solar effect. I used YOUR choice of data and YOUR choice of methods (as best I understand them) and I found no such results.

How can that possibly be of no interest to you? I’ve shown that your claims are incorrect using your data and your methods, and your comment is “I couldn’t care less”? …

Willis – Thanks for your reply to my last comment. I do accept that you have looked very diligently (and skilfully) at just about everything you can think of, and that the signal has not been there. I also accept that given your extensive efforts and others’ the probability of the signal existing is (to put it very conservatively!) small. Many thanks for trying, and many thanks for posting it all here. It’s part of what makes WUWT such an interesting and informative blog. [Thinks : if something that could be helpful to sceptics is demolished on a warmist site, there’s always the suspicion of bias. On WUWT it’s credible.]

Steven Mosher – I find your sneering comments illogical unjustified unworthy and unbecoming. Willis is looking for something, and he can’t find it, so others make suggestions as to where else he could look. So they are being helpful. Willis has looked in all the suggested places, and the thing he’s looking for isn’t in any of those places either. In all of that process, no-one has necessarily had any belief about whether the thing existed or not, an open mind is all that was needed. You are the one with the closed mind that is out of order.

I still think that Willis’ “Regular cycles which are apparently real appear, last for a half century or even a century, and then disappear for a century …” could be relevant. Does it necessarily mean that all such cycles are, in terms of what causes them, an illusion, or does it mean that when conditions change then effects change too? Even if effects disappear at times, wouldn’t they still be visible when averaged over the full record? The reality is that (a) I have to wait for someone to find the answer, or (b) Willis has found the answer (it’s an illusion), or (c) I have to find it myself. The last option, regrettably, is very unlikely.

Greg Goodman says:
June 8, 2014 at 4:16 am
——————————–
“My impression (which could obviously be mistaken) is that you failed to get any credible results or never even got as far as constructing the experiment.”

Greg, steady on. You are mistaken. I believe you may have mis-interpreted my comment about publishing results. I showed you not just a jpg of the revised experiment design, but a photograph of the very first of these experiments I built. This involved reflecting IR back to the surface of warm water. Results were published at Talkshop. My point about publishing results is this – one persons results carry far less weight than other persons replicating experiments.

I was not asking you to do the donkey work and run an experiment I had not run myself. Rather when you asked for proof, I gave you what I believed was the best proof possible, instruction on how to replicate the experiment.

I struggle to think of any more solid proof than an experiment you can replicate for yourself.

If you build the initial version of the experiment with IR reflected back to one cooling sample you will achieve ~1.5C divergence in ~30min in the evaporation constrained run. If you use a constant strong IR source as shown in the second version you will achieve over 5C of divergence. But I am not asking you to take my word for it. I am showing you how to check for yourself, just like Genghis is doing with IR thermometers and SST.

If you properly folded the data, than my apologies. I assumed you didn’t because the folded graphs have “years” and not a phase for their x-axis (which is still problematic towards the end of the cycles, besides being misleading).

Cheers
Nir

Thanks for that, Nir. I don’t know if I folded the data “correctly” or not. It’s yet another example of the necessity for archiving your code. Your description is not entirely clear.

In your Figure 5 you are using an 11-year cycle. Unfortunately, there are a couple of ways to do that.

One way is the way I took. I set the start of the fold at the minimum, and I just graphed out the next 11 years. As you point out, with varying length cycles this is problematic towards the end.

It sounds from your description in this comment as though you might have done it the other way. This is to interpolate an e.g. 13-year cycle to fit it neatly into an 11-year series of bins. I don’t use this method myself, because it creates an artificial situation—not all of the cycles are the same length, but your method assumes that they are. You could use the same method to jam any disparage group of cycles of any length into a new cycle of any length … I just don’t see the theoretical justification for that procedure.

However, none of this is of import because your own figures do not show a statistically significant result. Only two out of the 11 averages of your folded data are statistically different from zero at a p-value of 0.05. Remember that at that p-value of 0.05, we expect to find one false positive every twenty trials. With 11 trials over the 11 years of your folded average, finding two such results is a totally unremarkable result. Finding two of them out of eleven has an overall p-value of 0.08, meaning your results are not statistically significant.

Now, you have stated above that you have used a chi-square test to show that your results are indeed significant, saying that you determined the significance …

… by calculating the chi 2 and the effective number of degrees of freedom

However, the clearest mention of chi 2 in your paper is where you say you use a “sinusoidal least χ2 fit”. You also describe “A χ2 fitting …” and “The solid lines are χ2 fits to a harmonic variation”, and that’s it for chi-square. In other words, you only describe using a chi-square fit. Nowhere that I can find do you say anything about a chi-square significance test such as you describe above.

Let me say again that this is not your fault. The English language is simply not sufficiently precise and unambiguous to program computers, or to describe the action of the programs. That’s why we have computer languages … and it is also why it is so important that the code be archived, to avoid just these types of questions.

As a result, since your code is not archived I have no idea exactly how you “calculated the chi 2″ or what you subsequently did with it. So I have to fall back on my own methods, which are to note that your result (only two out of the 11 of your folded averages are significantly different from zero at the p=0.05 level) is not at all unusual or statistically significant, it is expected … and it gets steadily worse when you use more than three solar cycles.

Finally, it appears that you do not understand the implications of subdividing the data and taking correlations with the parts. You point, for example, to the fact that the correlation is better with the Atlantic portion of the OHC data than with the Global data of which the Atlantic data is a part.

However, in general this is true for any dataset. If you split the dataset, one resulting subset will have a greater correlation with any given X than the other subset, they won’t be the same. You seem to think this is meaningful. And indeed, it may well be very meaningful … or not.

The part I can’t find any sign of you accounting for in your calculations is that now, instead of having one trial looking for a significant result, you have three trials (correlations with the full data, with the Atlantic data, and with the non-Atlantic data). And if you continue that process of subdivision, sooner or later you will find a result that is “significant” at say a p-value of 0.05 … so what? If you keep looking long enough for something with a one in twenty chance of a false positive, before long you will assuredly find it.

Now, the way folks deal with this issue is to notice that the more places you look, the more unusual your results need to be in order to be statistically significant. Finding a one-in-twenty result (p=0.05) is meaningless if you’ve conducted twenty trials, that’s what you’d expect to find.

A good approximation of the need for better and better results is that for statistical significance, you need to find a result that has a p-value which is equal to the single-trial p-value (typically 0.05 in climate science), divided by the number of trials. So in your case, with three trials, you need to come up with a result having a p-value of 0.05 / 3 ≈ 0.017 …

w.

PS—As I said in the head post, I would have handled the folding differently. Rather than use your method, which allows you to e.g. shoehorn a 13-year cycle equally easily into a 9-year or an 18-year interval, I would have aligned the data at the peaks, and then looked at six years before and six years after the peak year. Averaging that stack uses real data rather than involve creating pseudo-data by cramming a 13-year cycle into 11 years, and it minimizes the end effects.

Here is a comparison of the of Hadcrut3 and sun spot numbers from 1950 through 2014. There is a distinct 11 year cycle evident with both. spectrum
I used data from woodfortrees.org and analyzed it with Audacity.
The 11 year cycle is less evident when including older data. Maybe the older the data the more crap it is.

…
And sorry, Willis, please don’t ask me to do the math first – I would not know a Fornier Transformation from a Ferret. But surely there is someone in the IPCC or in academia that does. Or, failing that, why not just pay Willis to look. Geeez, in terms of the amount of money wasted on climate ‘science’, giving Willis a stipend and an office would be a drop in the (warming or cooling) ocean.

Ralph

Thanks for that vote of confidence, Ralph. At times I think a stipend would be great … but then I realize that I’ll get slammed for my funding source, whatever it might be. Also, if I’m paid I’m obligated to write, and I don’t like that feeling. Even if the funder said “Just write when you want to”, it wouldn’t work—if I take the money, I feel obligated. This way, I’m free to write when and as I please, and pick and choose the subjects of interest … well, except when I get pulled back into the joy of the solar cycles, at least …

Crispin in Waterloo says:
June 8, 2014 at 9:19 am
———————————–
“Something else worth mentioning is that ocean water is quite reflective as well as having a high emissivity. I do not believe the story about water having an E of 0.7 – I have measured too much water with an IR thermometer to accept such a low number. The emissivity of water is in the high ’9′s’.”

This is getting a bit off topic, but I have been conducting some recent experiments into this issue. These involve measuring water surface temp with an IR thermometer under a cryo cooled “sky”. I can only get down to -40C at this stage. But with background IR minimised I need to adjust emissivity down to below 0.8 to get a reading matching surface thermocouple. An emissivity setting of 0.95 is fine for environmental measurement of water, but if that figure were used for calculating the radiative cooling rate of the oceans in the absence of atmosphere….

Willis, the buoy’s don’t measure the surface temperature, the skin where the evaporation and radiation actually takes place. They measure the water temperature just under the surface which clearly shows the night vs day pattern, just as the air temperature above the surface does too.

Thanks, Genghis. Your claim is that the water just below the surface goes up and down in temperature … and the air just above the surface goes up and down in temperature … but the surface stays exactly the same, except for wind?

I fear that falls into the category of “extraordinary claims require extraordinary evidence”, particularly since scientific researchers find otherwise. There’s a good discussion of the issues in this PDF. Inter alia, they say

The relationship between the temperature in the thermal skin layer and the sub-skin temperature just below the surface is reasonably well behaved …

So they don’t find the result you mention. But take a look at the document, it is an in-depth discussion of the issues.

In addition, you should look at the UK Met Office discussion of the conversion of skin temperature to bulk temperature, which is here. As the other document said, the relationship between skin temperature and the lower layers is well-behaved enough to successfully model it. This is important, because the satellite surface temperature measuring is only measuring the skin temperature. So to be compatible with the normal SST, they use the calculations to convert from skin temperature (at a given time, place, and wind speed) to bulk temperature … again, well worth reading.

I am extremely familiar with the buoy data, as well as all of the weather forecast models, Chris Parker, satellite data, etc. etc. As the Admiral gets very upset if the sailing conditions are not to her liking and my number one goal in life is to keep the Admiral happy.

Willis: “In any case, what I’m looking for is not a study. It is a dataset and a corresponding procedure for extracting the 11-year cycle. ”

Since we are all agreed that the “solar cycle” is it not a single fixed period no one is going to be able to extract such thing even if there is a solar signal to be found. It has to be linked directly to some proxy of solar “activity”.

As I have said many times, the fact that the solar cycle is irregular does not prevent us from detecting it in the sun, using any one of a number of methods. As a result, the claim that the effect of the cycle is magically undetectable in climate datasets for unknown reasons won’t fly.

Calculating for significance is as fraught with misguided creativity as calculating for linear trend lines. Trouble is, those who fail to understand the underlying math and proofs of data analysis think they can come up with a facsimile and call it good.

One of my favorites is a linear trend line through noisy data that was calculated by subtracting the first data point from the last data point and dividing by number of weeks between them to come up with the linear trend function. And then based on that result, use the function to substantially determine whether or not a student has a learning disability. When I protested, and stubbornly insisted that they should use linear calculations that were valid and reliable for noisy data instead of the made up of whole cloth calculation, I was threatened with a one day suspension.

Please folks, in this challenge by Willis to come up with something, don’t play around with statistical significance. Be conservative and use the gold standards.

RH says:
Here is a comparison of the of Hadcrut3 and sun spot numbers from 1950 through 2014. There is a distinct 11 year cycle evident with both. spectrum
I used data from woodfortrees.org and analyzed it with Audacity.
The 11 year cycle is less evident when including older data. Maybe the older the data the more crap it is.

====

I note in your “spectrum” link they both show 5.5y too. That is part of what makes up the shape of the solar cycle which rises quickly then tails off.

The late 20th c. period is one where it “works” which is why the question of cherry-picking arises. As you note earlier it works less well. It may have something to do with sampling biases in earlier data or just as likely the speculative “corrections” that are done to the data, mainly I think it is that the SST record quite a mix of cycles:

There is a fairly strong circa 9y component in most ocean basins. As this drifts in and out of phase with 11y cycles, as time progresses it will either add to or disrupt the 11y signal. This artificially increases it in much of the latter part of 20th c. and pretty much destroys it or pushes it out of phase in pre WWII period. Add to this that the “11y cycle” is a triplet of three close frequencies also interacting with each other and changing the profile and height of the solar peaks themselves plus a smaller 22y component.

As far as I have been able to tell, the 9y signal is similar to but a bit larger than the solar signal. About half of what appears to be solar correlation when it “works” is in-phase contribution of 9y.
The “9y” cycle seems more stable than the complex solar signal, it appears to be 9.05 to 9.1y , with various authors giving various margins of uncertainty, I suspect it is quite close to that central value.

All this means that tracking down any match to the solar signal is not going to just jump out of the page as some seem to expect it will. “It’s the sun stupid” is well, stupid.

IPCC seem to favour 0.1K pk-pk variation in surface temps over the 11 year cycle. It may be a fair bit stronger but that is order of magnitude concerned. We are looking for that against a record with annual swings about two orders larger with opposing phases in asymmetric hemispherical variations and 6mo tropical seasons. Plus all the rest of the churning climate system.

The school of AGW says it’s GHG+stochastic , in that ‘one variable’ model and under the assumption the rest is chaotic, the red noise test makes sense. Under a model that anticipates solar, lunuar, anthro and other ( possibly driven ) factors + noise , the individual components will be small and will not be “significant” against a simplistic statistical model where everything else is red.

Neither is unconstrained red noise a suitable model for variation in variables that are not free to do a ‘drunkards walk’ across the park but are instead bounded by negative feedbacks to remain on the path.

W: “As I have said many times, the fact that the solar cycle is irregular does not prevent us from detecting it in the sun, using any one of a number of methods. ”

Agreed, my point was that is it not a simple fixed 11 years.

W: “As a result, the claim that the effect of the cycle is magically undetectable in climate datasets for unknown reasons won’t fly.”

Is that supposed to relate to my quote? I did not say it was, I said it has be sought as directly to the solar signal , because of its irregular nature.

How are you getting on with interpreting the 0.25 correlation coefficients? What does your ‘program’ give for circa 1700 data points?

If you are having trouble with my cross-correlation, do your own with the full monthly data, without averaging, “binning”, detrending, just a straight CC. Correlation is simply and uniquely defined and calculable at each lag value.

Do you still think it is necessary to detrend before doing CC ? That would seem to be an error to me in the context of assessing magnitude and timing of peak CC.

w., I don’t remember, did you look at CERES SW outgoing radiation and solar activity/cycle?

Also, I would expect that ocean circulation patterns would dominate the SST and that increased SW in the ocean may very localized. Perhaps it would be good to look at solar activity between el Nino events.

Konrad: “Results were published at Talkshop. My point about publishing results is this – one persons results carry far less weight than other persons replicating experiments. ”

I first came across your name on TS, I recall similar vaporous claims with no numbers. If the “proof” is over there, I must have missed it, please link. So far there’s nothing to “replicate”. One person’s _results_ have far more weight than one person’s ” I can assure you”.

While this is a simple experiment just reflecting IR back to cooling samples, it suffers from the fact that backscattered IR is dropping as the water cools. This is why I show the build for the later versions as they use a constant IR source and results are more exaggerated.

There really is nothing special about such experiments. They are just the IR version of the old hair dryer trick –

Q. How do you heat a plastic tub of water with a hair dryer?
A. Point the hair dryer at the side of the tub, not the water surface.

DWLWIR combined with an average wind speed of Beaufort scale 4 is like trying to heat the oceans with a hair dryer.

The question you should be asking is not “have I run the experiments?” But – “if DWLWIR is not slowing the cooling rate of the oceans, what is keeping them warmer than the -18C predicted by SB equations?” The experiments to answer this are far more fun. Ever imagined you could run a steam engine off a flat plate solar collector with no concentration?

I grant that your analysis is correct and there is correlation between solar cycle and SST and land temperatures. You quoted a peak to peak variation of 0.08 to 0.1 C between solar maximum and minimum. But isn’t the annual variation can be three times bigger than this? 0.1 C change in 11 years looks like noise. Whatever is causing a bigger variation in just one year can certainly cause a smaller variation in 11 years.

sun is .000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000
to the 100,000,00 million/th power.
Heck the sun may have had a divorce 1/2 that time ago.
May be calling an atty just now to cut the child support.
We being the child.

Greg Goodman says:
June 8, 2014 at 8:08 pm
——————————-
If you build the second variant with a constant strong IR source, there is a way to get water to heat via LWIR. The trick is to start with water that is very cold, so little evaporative cooling is occurring.

A good comparison test is to replace the water samples with matt black aluminium blocks. While not directly comparable as aluminium has a far lower specific heat capacity than water, the response to LWIR is distinct. According to climate models the oceans should be responding to DWLWIR as a “near blackbody” similar to the aluminium blocks. This is clearly not the case. All climate models show DWLWIR warming the oceans by ~33C above their theoretical blackbody temp. Something is very wrong with this picture.

As a matter of interest, Willis, will your many analyses of the data show up a variable cycle, like the Sunspot Cycle – which appears to vary between 10 and 14 years? Does a FT of the raw SSN data, for example, show a prominent peak at 11 or so years?

Assuredly yes to both questions. Also, the “folding” technique reveals the variable sunspot cycle, no problem.

Willy
I have followed with interest your efforts to detect the 11-year solar cycle in meteorological data and the lack of evidence for any correlation. So I am wondering whether longer-term variations in solar activity have had any effect. The 14C and 10Be radioactive isotopes provide good proxies for solar activity.

Actually, since I can’t find the ~11 year solar cycle in the 10Be data, I’m not at all convinced that 10Be is a “good proxy”, I mean, how good can it be if it can’t detect the 11-year cycle? See Cosmic Rays, Sunspots, and Beryllium for a full discussion.

it is obvious that the goal of this thread is not to determine whether there is an eleven year cycle evident in the record, it is just to belittle those looking for it. it really is not a simple task. there are just so many possibilies there. to claim that one knows without doubt that it should be most visible in the the 11 year cycle is a clear failure and outright arrogance. it is this form of arrogance that we see every day in the climate science community. it is unexpected from the engineering community, which i thought willis was from.

mobihci, like many of the children infesting the internet, you seem to be incapable of reading. I said quite clearly:

My Usual Request: If you disagree with someone, myself included, please QUOTE THE EXACT WORDS YOU DISAGREE WITH. This prevents many flavors of misunderstanding, and lets us all see just what it is that you think is incorrect.

Of course, the hordes of interchangeable arrogant anonymous popup internet jerkwagons like yourself always think you are above such common politeness.

As to whether the goal of this thread is to determine whether there is an eleven year cycle evident in the record, I have asked repeatedly for you or anyone to come up with such a cycle. No one has done so to date. Instead, like yourself, they want to cover up their pathetic failure to do what they often claim is a simple task by attacking me.

As to whether or not finding such a cycle “really is not a simple task”, Nir Shaviv and many others claim that it really IS a simple task. So you’ll have to take that question up with Nir and others who make such a claim …

Unfortunately, when I try their own methods on their own data as I did with Nir’s methods and data, the results quickly evaporate. So perhaps the task is more difficult than they claim. Me, I’ve made no claim that it should be easy OR hard. What I have said is that after several centuries of looking and only finding the most elusive hints of an 11-year solar cycle in the data, we can say that if such a cycle exists, it is buried way down in the weeds.

Now, if you want to arise from your moribund okole and contribute to the thread by finding such a cycle, please do so. However, sitting around and whimpering that you don’t like me and my results is just pathetic. It’s hard for folks to watch a grown man acting as childishly as you are. Lead, follow, or get out of the way, but whining about how you don’t like me is useless.

“What your cited graph actually looks like, however, is that you forgot to detrend your datasets before doing the cross-correlation …”

The slope affects the correlation. If you want to know where the max correlation happens and what it’s value is, why would I want to distort both datasets by removing a spurious linear trend from both before doing CC?

I appreciate your asking, but if you don’t know the answer to that question, it’s clear you are far beyond my poor powers to assist you.

If the suns traveling at a speed of 750,000 kmph on its path around the galaxy and the sun gets out in front like when we see an eclipse hear on earth and we have a strong energy exchange like a full moon here on Earth . Does the earths induce a space quake? http://www.everythingselectric.com/forum/index.php?topic=245.0

Greg: The slope affects the correlation. If you want to know where the max correlation happens and what it’s value is, why would I want to distort both datasets by removing a spurious linear trend from both before doing CC?

Willis: “I appreciate your asking, but if you don’t know the answer to that question, it’s clear you are far beyond my poor powers to assist you.”

Is that an elaborate of saying ” I don’t know, so I’ll pretend to be superior and walk away”? That is a response worthy of 1sky1. Surprising, you’re normally so good at telling everyone how they should be doing things.

How are you getting on with the significance question? I thought you just needed to number of data points to plug into an R function call. For the central region N=1700 for both datasets, what do you get for your 95% test ?

(NB: in early years in particular before 1749, the means are computed on only a fraction of the days in each year because on many days, no observation is available).

Offhand I don’t see you using the pre-1749 annual data in your previous few posts, so why include it in your file, especially without the caveat?

There is also a strange difference in Leif’s file, which may be related to old-style programming. When comparing in the 1749 to 1945 or ’47 range, SIDC times 1.2 then rounded to 2 decimal places yields exactly your numbers.

But starting in 1849, Leif usually varies by some multiple of 0.12, the differences range from +0.36 to -0.48. As 0.12 comes from rounding 1/8 (round to the even #), it’s like someone was saving file space by using only 3 bits (or 1 digit) to store the decimal portion as eighths.

From what I have seen in science of using arcane file and record formats that need to be read by a proprietary Fortran program, I wouldn’t be surprised.

Greg Goodman says:
June 8, 2014 at 6:04 pm
——————————–
“…The question you should be asking is not “have I run the experiments?” But – “if DWLWIR is not slowing the cooling rate of the oceans, what is keeping them warmer than the -18C predicted by SB equations?” The experiments to answer this are far more fun. Ever imagined you could run a steam engine off a flat plate solar collector with no concentration?”
///////////////////////////////////////
Konrad

Personally, I consider it wrong to consider the temperature of the oceans to be about 18degC. In reality, the average temperature of the oceans is about 4 degC. I would suggest that when considering the so called Green House Effect, we should use a figure of 4 degC for the ocean temperature.

I would therefore rewrite your question (and I have previously posed this several times on other threads), why after approximately 4 billion years of Solar + DWLWIR are the oceans only 4 degC?

It is only by chance that we are presently seeing the SST at about 18 degC. In other planetary cycles, we would be seeing this as about 23 to 26 degC, or as 2 to 5 degC, and this not because of a huge difference in the amount of solar irradiance that is hitting planet Earth or in the amount of DWLWIR.

We would not experience ice ages, at least not in the way we have seen them, if the average temperature of the cocean was 18 degC. It is because the average temperage is so low (about 4 degC) that it comes back to bite..

I would therefore rewrite your question (and I have previously posed this several times on other threads), why after approximately 4 billion years of Solar + DWLWIR are the oceans only 4 degC?

Gravity!

As I heard it explained, as water cools it will shrink in volume, but only until it reaches approximately 4°C for pure water, when it achieves maximum density. Colder than that, water will expand.

As gravity causes the water above to force the water below into the smallest possible volume, it forces the water below to be about 4°C, the temperature of the minimum volume. Thus the depths of the ocean are that temperature, down to where it rises from lithospheric heat.

This also makes claims of “missing heat” to be questionable. As gravity maintains the deep water temperatures at a fixed amount, it is very difficult to warm the ocean depths at all. If the heat cannot be absorbed, and as the lithosphere underneath is a source of heat, logically the ocean depths should reject the extra thermal energy and “bounce” it back upwards.

Willis, thank you very much for the two links. I spent a while digesting them, because they use different definitions of the surface layer and come to slightly different conclusions. But both seem to be in general agreement with my claim, that wind controls the surface temperature.

If you look at the PDF cartoon Figure 1 it shows a relatively constant surface temperature at night OR in STRONG WINDS. Their definition of strong winds is 12 knots, it is typical for me to see 15 knot winds for weeks. With no wind or very light winds the surface warms during the day.

Which is exactly what I am claiming, that the wind speed controls the surface temperature, but the two discussions help clarify the mechanisms and emphasized mixing and thermal inertia.

What I found extremely interesting in the PDF was the suggestion that

“The variability of near- surface temperature gradients can be large compared to the accuracies of SST needed for climate research (e.g. Ohring et al. 2005), and thus the effects of the thermal skin layer and diurnal heating must be taken into account when generating CDRs of SST. For this reasons, a SST foundation temperature is defined and suggested for use in climate applications.”

Their SST Foundation temperature is found below 10 Meters, below the diurnal and mixing layers and which incidentally is the same temperature as the surface temperature in strong winds. Who would have thought?

You form an interesting hypothesis that if plausible, would certainly call into question the whereabouts of the missing heat speculation. Lots of discussion points to consider. Do you have links? Or is this your own thought? Either way, I see a really good blog post in the making if you care to. I for one would readily peruse your expanded post.

Willis Eschenbach
We know when sun spots are big the release CME’s that can hit earth with lots of energy(even ice structure changing ones)
This may be a dumb question but is there a complete index that measures all the energy the sun releases and hits earth.

The slope affects the correlation. If you want to know where the max correlation happens and what it’s value is, why would I want to distort both datasets by removing a spurious linear trend from both before doing CC?

Willis:

“I appreciate your asking, but if you don’t know the answer to that question, it’s clear you are far beyond my poor powers to assist you.”

Is that an elaborate of saying ” I don’t know, so I’ll pretend to be superior and walk away”? That is a response worthy of 1sky1. Surprising, you’re normally so good at telling everyone how they should be doing things.

Nope. It’s a simple fact. If you don’t understand why you would detrend a pair of ~150 year datasets when looking for an 11 year cycle, I’m quite sure I will have no success explaining it to you, particularly given your instant knee-jerk disagreement with almost anything I say. Ask your favorite statistician, Greg, because truly, I can’t help you.

How are you getting on with the significance question? I thought you just needed to number of data points to plug into an R function call. For the central region N=1700 for both datasets, what do you get for your 95% test ?

Since your results are meaningless when looking for 11-year cycles (due to the fact that you did not detrend your data), there is no way to calculate their statistical significance.

Willis Eschenbach
We know when sun spots are big the[y] release CME’s that can hit earth with lots of energy(even ice structure changing ones)
This may be a dumb question but is there a complete index that measures all the energy the sun releases and hits earth.

First, on my planet, the only dumb questions are the ones I don’t ask, ’cause then I won’t find out the answer …

Regarding your question, I’m not sure it can be answered because we first need to define “hits the earth”.

In terms of CMEs, the index usually used is the solar wind. You’d think that if you multiply the energy in the solar wind by the cross-sectional area of the earth, that’s how much hits the earth. However, much of this energy is captured in the upper atmosphere … so do you include that as “hitting the earth”?

In addition, the sun also puts out energy in the form of a complex time-varying magnetic field. THis interacts with the magnetic field of the earth, and since the earth is rotating, there is a net transfer of energy … but I don’t know if you could call that “energy hitting the earth”. Not sure how you’d measure it, either … you need someone like Leif Svalgaard for that question.

Willis: “…. given your instant knee-jerk disagreement with almost anything I say.”

No, Willis that’s in your head. Much of what you produce is fine work. Sometimes very insightful. That does not mean I’m not going to say when I think something is wrong. As I’ve explained before my aim is improve what you have to move it forwards, not to tear it to bits to dismiss it as you seem to enjoy doing with the work of others.

Maybe you’re projecting your motivations onto me. In either case you’re mistaken about what drives me. Now let’s look again at what you have to say about the data.

Willis: “Nope. It’s a simple fact. If you don’t understand why you would detrend a pair of ~150 year datasets when looking for an 11 year cycle, I’m quite sure I will have no success explaining it to you, particularly given your instant knee-jerk disagreement with almost anything I say. Ask your favorite statistician, Greg, because truly, I can’t help you.

Since your results are meaningless when looking for 11-year cycles (due to the fact that you did not detrend your data), there is no way to calculate their statistical significance.”

Ah, you’re a classic Willis. This is what your pretence at objective science has come to? Any twist and devious argument rather that accept what the data shows. So you can soldier on : “can’t find solar here, can’t find there….. well try as I might … I’m waiting for someone to show… blah blah”.

So there’s a clear correlation between hadISST and SSN but you refuse to calculate whether your ‘program’ says it significant because it’s not tuned to look only for an “11y” signal at the exclusion of all else, even though the first peak is at about 11y, the second about 22y and there is an overall correlation showing a peak at 11y lag.

Well let me help you out Wiilis. Your post showed 0.2 ( figure 2) when you wanted to take out Shaviv’s paper. From figure 1 we see you used the full data as I did except that it clearly was not monthly resolution, it’s annual. That means I have about 12x as many degrees of freedom as you had.

To cut a long story short that will notably reduce significance threshold but to avoid further argument lets just say it will be 0.25

So, after months of tireless work we have finally found something.
====

(NB: in early years in particular before 1749, the means are computed on only a fraction of the days in each year because on many days, no observation is available).

Offhand I don’t see you using the pre-1749 annual data in your previous few posts, so why include it in your file, especially without the caveat?

Mmm … I’ll have to look into that. I’ve assumed (without actually checking it, always dangerous) that even if you only took observations one day in three or four, that the annual sunspot average would not be affected much. Hang on … … … OK, here’s what I did. I took the daily sunspot data and averaged it annually.

Then I took the same daily data, and knocked out 90% of it randomly, leaving only 10% of the observations, on average only one every ten days. I then annually averaged that decimated data, and plotted it. I did that 100 times. Here are the results:

As you can see, even if we knock out 90% of all the daily observations, it doesn’t affect the annual averages all that much.

You will recall one of your excellent posts about how models were just smoke and mirrors wrapped around a linear. You produced a graph that showed what happened in response to a constant increase dRad: it created a constant ramp in temperature, but only after a certain delay related to the time-constant tau of the system.

The system integrates the change and if the change is constant, after a few time-constants the system settles to a fixed response. Usual e-folding rules: after five tau it’s within 1% of its final value. After 3 tau 95%.

Following on. So in general there is a lagged response of a different form to the changes in the input. So it’s not a simple fixed time offset, but its of the same order as one or more tau. One way to calculate it is convolution with suitable exponential decay kernel, another a simple one-step recursive calculation.

“Tau typically has a value of between 3 and 4.5 years to emulate the forced response of AOGCMs over the instrumental period, but this needs some severe health warnings. There are many instances where it is not appropriate to assume a constant heat capacity model.”

Now due to various negative feedbacks in the system and the depth that the effect penetrates into the ocean there will be different time constants in a more realistic model. Daily and even annual excursions will have large amplitude and affect just the mixed layer.

Decadal cycles will, by diffusion, start to penetrate below the thermocline, invoking a much greater mass of water and longer time constants.

This will integrate changes in the magnitude of the solar cycle severely attenuating the circa 11y variations but retaining a long term rise or fall in solar activity. Clearly any naive attempts at regression against monthly or annual SSN will totally fail to match this response curve. However, it may manifest in a lag correlation plot.

As you can see, even if we knock out 90% of all the daily observations, it doesn’t affect the annual averages all that much.

Actually, my problem with it was the pre-1749 is whole numbers, the rest from SIDC is one decimal place. That had messed up my spreadsheet comparisons.

If you’re worried about significant digits, then they are separate datasets, do not commingle without appropriate notations and caveats. I had to use different rounding. Different equations for different results in the same column is often frowned upon.

BTW, you and Leif are both guilty of claiming too much significance, as you take numbers to the tenths, multiply by a constant, then give results to the hundredths. The non-multiplied post-1748 stays at tenths.

The last layer is the deep-water layer. Water temperature in this zone decreases slowly as depth increases. Water temperature in the deepest parts of the ocean is averages about 36°F (2°C).

Even the Challenger Deep in the Mariana Trench, the deepest known spot in all the oceans, is not noted for having ice at the bottom, but instead water is there. Note the pressure is reported to be 111 MPa (mega pascals).

Now water ice has 15 known solid phases. However, if you look at the log-lin pressure-temperature phase diagram at the link, you’ll see that while higher pressures will force a change to ice, 111 MPa is actually in the range where pure water can exist as liquid while below 0°C. While there are some solid phases that would be dense enough to stay at the bottom, none of them form in the deep ocean conditions, not enough pressure.

If you’re going to pull the ‘But it’s not fresh water’ line, given the lowered freezing temperature to about -10°C (by zoom and eyeball), I’d like to see supporting documentation please.

If there was ice formation, then wouldn’t there be brine rejection, moving it to a higher freeze point? With the lower salinity water freezing at the bottom, would the resultant higher salinity water go under the ice, leading to ice over water under water? Or would the excess salinity precipitate out and accumulate on the bottom? Now that would be silly, as the salts would wind up sequestered along the bottom, leading to decreasing ocean salinity over time if land runoff and minerals dissolved from higher-up marine deposits couldn’t replace what was sequestered.

Little progress can be made in examining the empirical relationship between
SST and SSN time-series–or most geophysical data–by clinging to
conceptualizations based on little more than linear regression. The latter
assumes INDEPENDENT trials of a dependent variable, whose outcomes are
plotted as a function of the perfectly known independent variable. It is
only there that any correlation between trials needs to be accounted for,
as mistakenly suggested in the caption to Figure 3, to prevent
OVERESTIMATION of the statistical reliability of derived trend and
intercept.

With noisy time-series that may have only concommitant variability, instead
of direct linear dependence, there will always be sample acfs for both
series, each reflecting their S/N ratios. Those noise levels have a
profound degrading effect upon the sample ccf, invariably UNDERESTIMATING
the actual level of coherence between signal components in nature. These
are fundamental matters of ANALYTIC insight, for which no tedious Monte
Carlo simulations are required for those equipped to do serious geophysical
signal analysis and physical interpretation.

Greg, I said that you had made a mistake in your calculations by not detrending the data before you did the cross-correlation. In response, you said, inter alia:

The slope affects the correlation. If you want to know where the max correlation happens and what it’s value is, why would I want to distort both datasets by removing a spurious linear trend from both before doing CC?

and

If you are having trouble with my cross-correlation, do your own with the full monthly data, without averaging, “binning”, detrending, just a straight CC. Correlation is simply and uniquely defined and calculable at each lag value.

Do you still think it is necessary to detrend before doing CC ? That would seem to be an error to me in the context of assessing magnitude and timing of peak CC.

and

Is a correlation of 0.25 at 10y and 22y less important now than when [you] wrote the article?

and

If you are having trouble with my cross-correlation, do your own with the full monthly data, without averaging, “binning”, detrending, just a straight CC. Correlation is simply and uniquely defined and calculable at each lag value.

Do you still think it is necessary to detrend before doing CC ? That would seem to be an error to me in the context of assessing magnitude and timing of peak CC.

and

Well let me help you out Wiilis. Your post showed 0.2 ( figure 2) when you wanted to take out Shaviv’s paper. From figure 1 we see you used the full data as I did except that it clearly was not monthly resolution, it’s annual. That means I have about 12x as many degrees of freedom as you had.

To cut a long story short that will notably reduce significance threshold but to avoid further argument lets just say it will be 0.25

So, after months of tireless work we have finally found something.

Thanks, Greg. It’s clear that you think that your results, with a “correlation of 0.25 at 10y and 22y”, establish the case for the existence of a an ~11-year solar cycle in the HadISST sea surface temperature data. And you invite me to do my own cross-correlation.

Well, I’m not the man to shrink from such a challenge, so I’ve done my own cross-correlation. And to my surprise, it’s almost identical to yours. I used annual data because that’s what I’ve been using all along in this thread, but other than that they are quite similar. First, here are your results …

And here are my results:

As I said, I was kinda surprised by that result. But I’m a scientist, I follow where the data leads, and just like yours, my results show peaks at 11 and 22 years, with a correlation of about 0.25. I gotta admit, you were right about that.

So, in your words, does this mean that “after months of tireless work we have finally found something”?

Unfortunately … no. To explain why the results are meaningless, let me show you the two datasets that I actually used for the cross-correlation shown immediately above:

NOW do you understand why I said that your procedure is meaningless, and that it does NOT establish your case for either an 11-year or a longer term solar effect? I get results indistinguishable from yours when the “temperature data” is a few straight lines and there is absolutely no 11-year cycle in the data anywhere.

Which is what I told you above, over and over. Test your procedure by running it against red noise, I said. Anything with a prominent peak or valley will give that kind of result with peaks at 11 and 22 years, I told you, so test your method against something like that.

But noooo, Greg is a brilliant man, he don’t need no steenkin’ tests, he just wants to tell me that I’m wrong.

As you can see, even if we knock out 90% of all the daily observations, it doesn’t affect the annual averages all that much.

Actually, my problem with it was the pre-1749 is whole numbers, the rest from SIDC is one decimal place. That had messed up my spreadsheet comparisons.

If you’re worried about significant digits, then they are separate datasets, do not commingle without appropriate notations and caveats. I had to use different rounding. Different equations for different results in the same column is often frowned upon.

BTW, you and Leif are both guilty of claiming too much significance, as you take numbers to the tenths, multiply by a constant, then give results to the hundredths. The non-multiplied post-1748 stays at tenths.

KD, a quotation of whatever you accuse a man of being “guilty” of is not optional. If you want to call a man “guilty”, then you need to have the stones to quote what the hell you are accusing him of. What you have done is just slimy mudslinging.

An apology would be appropriate. Calling a man “guilty” of anything, no matter how small, without any attempt to provide even a scrap of evidence is … is … well, I won’t say it, this is a family blog, but it is not the act of an honorable man.

While that is true for pure water at atmospheric pressure, it is not true in the ocean depths. There, the water continues to contract until it freezes.

If that is true then Jacques Cousteau was a lying Frenchman as he never showed me any deep sea ice and talked of liquid water straight to the bottom.

Sorry, that’s my lack of clarity. What I meant was that at the salinity of the ocean, sea water keeps contracting all the way down to freezing temperature. I didn’t mean that there is a layer of ice on the bottom of the ocean, because there isn’t one.

However, it appears you don’t believe my claim. If so, please take a look at this document. The crossover point for density, salinity, and freezing point is shown in Fig. 5.1. It occurs at a salinity of 0.25. Since the ocean is saltier than that, sea water continues to increase in density until it freezes.

Little progress can be made in examining the empirical relationship between
SST and SSN time-series–or most geophysical data–by clinging to
conceptualizations based on little more than … etc. etc.

Gotta love 1sky1, my favorite random anonymous internet popup. The thing about him is that he has a perfect record. Every single comment of his is 100% bafflegab, not one citation, never one quotation, not one fact … just a lot of claims about how brilliant he is and how wrong everyone is who disagrees with him.

And this comment of his continues his flawless performance. This is good, it means the world is unfolding as usual, no surprises.

Good work Willis. So what you’ve demonstrated is the long term variation in ISST shows correlation with SSN that peaks somewhere between 11 and 22y lag (ignoring the short term bumps) that is significant according to the test you adopted in figure 2. This it what you said I “forgot” to remove when looking for correlation.

Then I did remove it and reported peaks at +0.2 and -0.2 correlation remain, contrary to what you showed in your figure 2. That too is presumably “meaningless” now that I’ve done what you suggested I should have done.

“Which is what I told you above, over and over. Test your procedure by running it against red noise, I said. ”

Well unless I’m mistaken ( because you have not stated where your 0.2 comes from or what it is supposed to represent ) that is a 95% confidence test against a random noise model. But I agree, I’m not even sure that test which is usually applied to auto-correlation is valid here. If you think some other test against red noise is needed then you should have done yourself instead of drawing a 0.2 line. Either it’s valid or it isn’t.

So what you are now saying is that the central test you used in figiures 2 and 3 to attack the work of Shaviv is “meaningless”. Or it only matters when it suits the point you wish to make.

Anyway, it’s a beautiful summer’s day which I don’t wish to spend on yet another Pythonesque arguement with Willis.

BTW, if we have to detrend to remove the long term correlation and any peaks that may appear at 11 or 22 are spurious artefacts that would equally be produced by random noise, why did you choose to do cross-correlation at all to test the validity of Shaviv?

If there’s no peaks it proves he’s wrong , if there are peak they’re spurious.

BTW, if we have to detrend to remove the long term correlation and any peaks that may appear at 11 or 22 are spurious artefacts that would equally be produced by random noise, why did you choose to do cross-correlation at all to test the validity of Shaviv?

Please provide a quotation to show that I claimed that “any peaks that may appear at 11 or 22 are spurious artefacts that would equally be produced by random noise”. I have never made that claim that I know of. That’s why we have tests of significance, to separate signals from random noise.

Let me ask again, since you seem resistant to the idea. Please quote the stuff you disagree with, so we can understand what it is you are on about. You’ve just accused me of something I never said. How can I defend something I never said, something that you made up?

QUOTE MY WORDS!!! Your fantasies about what I’ve said are getting very old.

Any attempt to claim that because an 11 or 22 year cycle cannot be determined in SST records is a dead end as to disproving solar influence on climate or saving WUWT from the embarrassment of attacking those who were right at Talkshop.

You did it. You can’t undo it. You just have to wear it.

If you incorrectly treat the oceans as a “near blackbody” instead of a UV/SW selective surface, you cannot possibly understand solar influence on climate.

The effect that needs to be detected is vanishingly small. 0.8C in 150 years. We just don’t have the data. No amount of bleating about 11 year cycles can erase WUWT’s premature “real climate” style dismissal of our variable star’s influence on climate.

To determine solar influence on climate two things are needed –

1. Long accurate records of ocean temps below the thermocline.
2. Long accurate records of incident UV-A at the sea surface.

We have neither.

Any attempt to disprove solar influence on climate without such records would be clearly disingenuous.

my involvement in this thread is not about individual efforts to find 11 year cycles or 22 year or whatever, i dont really have any position on the subject of solar min/max. i do however believe that SSNs and the sun in general influence the climate, just not through the methods you have so far covered.

i quoted the problem i have with this thread, and it wasnt in the post itself, but the comments. but if you must have something from the post it is this-

“What I am saying is that I still haven’t found any convincing sign of the ~11-year sunspot cycle in any climate dataset, nor has anyone pointed out such a dataset. And without that, it’s very hard to believe that even smaller secular variations in solar strength can have a significant effect on the climate.”

you leave this as an implication that the subject is resolved, and of course mosher makes it a statement. but i find this statement just amazing in terms of how complex the system actually is.

the truth is that it is VERY likely that the variations hide in noise. i dont understand why you believe they need to be visible in the noise. this just does not make sense unless you have perfect records for everything (including the various cycles of the sun, the oceans, the atmosphere, the ‘currently unknown influences’ etc) over thousands of years in which case you could tease out longer modes and calculate the higher frequencies from that. we dont have perfect records, we dont understand even a fraction of the biospheres influences or how their cycles may overlap etc, so this just will not happen.

my point is that when you say-

“Let me be clear about what I am saying and not saying here. I am NOT saying that the sun doesn’t affect the climate.”

you should mean it, because it may be that it is impossible to ever see this in data as we know it/have collected it in the last few decades. it may be that the only way to work out the influence the sun has on climate is to work on the mechanism. the how, not the if. the if as far as i am concerned is broadly answered by history and multiple proxies- yes the sun does affect the climate.

willis, my tag is not anonymous. it may be to you, but a lot of people i know personally know it and i have only ever posted/replied etc under that one tag, so keep your abuse to a minimum please. my response to this thread in general that you replied to was me being sick and tired of snark comments from mosher which you seem to applaud and seeing a red flag. perhaps you are seeing too many red flags too, but if you truly are interested in finding the WAY the sun CAN influence the climate, then i apologise for implying that you may not be.

KD, a quotation of whatever you accuse a man of being “guilty” of is not optional. If you want to call a man “guilty”, then you need to have the stones to quote what the hell you are accusing him of. What you have done is just slimy mudslinging.

Excuse me! I thought it was clear because that was the small thing we were talking about.

To quote myself: “BTW, you and Leif are both guilty of claiming too much significance, as you take numbers to the tenths, multiply by a constant, then give results to the hundredths.”

We were talking about the SIDC SSN data. The only spot where Leif and you were multiplying by a constant was the +20% correction range, multiplying by 1.2, a constant.

So in your data, “Shaviv Folder.zip” -> “Shaviv Folder” -> “SIDC Sunspots.csv”, there is for example:1749,97.08
1750,100.08
1751,57.24
1752,57.36
1753,36.84
where SIDC in “yearssn.dat” has:1749.5 80.9
1750.5 83.4
1751.5 47.7
1752.5 47.8
1753.5 30.7
There it is. SIDC reported tenths, you gave results to the hundredths. Leif in “SSN-HMFB-TSI.xls” did the same.

Quoting myself again: “The non-multiplied post-1748 stays at tenths.”

And indeed, near the end of “SIDC Sunspots.csv”, you can see the transition:1944,11.64
1945,39.84
1946,111.12
1947,151.6
1948,136.3
1949,134.7
Leif’s Excel file shows the same transition but starting in 1945. Thus as I succinctly said, the non-multiplied post-1748 stays at tenths.

Thus both you and Leif have a significance issue, where you are both guilty of claiming too much significance, by taking numbers to the tenths, multiplying by a constant (1.2), and reporting hunderdths.

However, as not quite a correction but more like an addition, I see I may have limited the scope of the charge too far. Pre-1749 from SIDC had a zero tenths, which a human should notice means very likely those are whole numbers as that is a special range and they all have zero tenths, thus it’s a record format issue.

Following the pattern you and Leif share would yield a zero hunderdths for the results of that range, which would be supressed on output as with other trailing zeroes. Thus the significance issue likely did happen pre-1749 as well, but for two orders of magnitude, but the evidence has conveniently disappeared.

konrad, you also need top of the atmosphere solar metrics. If all you have are surface solar indices, no matter how long or accurate, you will not be able to rule out Earth’s own atmospheric source of variation in solar input measured at the surface over land or sea.

While that is true for pure water at atmospheric pressure, it is not true in the ocean depths. There, the water continues to contract until it freezes.

I showed at the ocean depths the water is not continuing to contract until it freezes. I didn’t believe your claim, I showed why. End, period, full stop. Now you clarified your statement, you changed the conditions, and implicitly say my rejection of your original claim means I appear to reject the revision.

You don’t get to write up a new contract with new terms and say my rejecting of the old contract means I apparently reject the new one. I’ll evaluate it on its own merits.

If so, please take a look at this document.

Ack! 4.5MB at 11 minutes estimated dial-up time for a single graphic. That’s a slick move worthy of Crypt-Mosh or Stokes, ‘Just look inside this giant tome for the proof you could examine in just seconds’.

And what is it with you crazy people who keep sticking spaces into folder and file names. I’ll need an escape sequence for the URL in a browser and a “backslash-space” for the command line. Sanity, people, sanity!

The crossover point for density, salinity, and freezing point is shown in Fig. 5.1.

Oh dear God, not another M$ PowerfullyPointless presentation crammed into a pdf. Which my simple non-Adobe reader doesn’t like, that I have to look at with the browser. Just post the dang ppt file, LibreOffice can handle it!

It occurs at a salinity of 0.25. Since the ocean is saltier than that, sea water continues to increase in density until it freezes.

(Psst, you’re a decimal point off, see Fig 5.1 caption.)

But just one slide back it says:

2/ For high Sal waters (S > 25), decreasing temperatures induce convection which continues without the state of maximum density being reached. The temperature decreases till the whole water column is at the freezing temperature. However, freezing of the whole water column can occur only in shallower water.

It’s a variation of the isothermal column of air. Warmer and less dense above, colder and more dense below, energy evenly distributed per volume of sea water.

So increasing density at depth won’t do it, not in the “mild” Earth deep sea pressure ranges. Energy needs to be removed from the entire column, until every horizontal slice of the column is at the freezing point relative to its depth, then the column freezes.

But in the deep water regions you can’t freeze the whole column from surface to the bottom, likely as the ocean is stratified. So you’d likely have a break at the main thermocline to deep water layer transition.

You had said:

What I meant was that at the salinity of the ocean, sea water keeps contracting all the way down to freezing temperature.

and

Since the ocean is saltier than that, sea water continues to increase in density until it freezes.

Perhaps a sample of seawater subjected to mega-pressures in the lab might yield ice, far higher than in the ocean depths.

But at those pressures, I have doubts the seawater would remain as it was. Checking up on precipitation fouling, I see that, depending on the substance, increasing temperature may increase or decrease solubility, likewise for decreasing temperatures.

Thus I strongly suspect pressure, within the giant range variations between merely deep sea to center of the Earth levels, could have a considerable effect on solubility on some of the solutes, leading to precipitation and a decrease in salinity, leading to more “normal” freezing with brine rejection.

I believe you and the author are deceived by the sunspots because it is not exactly the sunspots that would have the effect on earth. Sunspots are the precursor to CME’s. I strongly believe that if you looked at CME’s during the past 3 cycles which were directed toward earth… you will find your answer.

kadaka (KD Knoebel) says:
June 10, 2014 at 4:08 am
From Willis Eschenbach on June 9, 2014 at 10:51 pm:

KD, a quotation of whatever you accuse a man of being “guilty” of is not optional. If you want to call a man “guilty”, then you need to have the stones to quote what the hell you are accusing him of. What you have done is just slimy mudslinging.

Excuse me! I thought it was clear because that was the small thing we were talking about.

Obviously not …

To quote myself: “

BTW, you and Leif are both guilty of claiming too much significance, as you take numbers to the tenths, multiply by a constant, then give results to the hundredths.”

We were talking about the SIDC SSN data. The only spot where Leif and you were multiplying by a constant was the +20% correction range, multiplying by 1.2, a constant.

You said that I “claim too much significance”. I don’t see anyplace in there that I “claim” anything. My practice is to never truncate the results of the individual calculations leading up to a result. So for example, if I’m multiplying, then dividing, then taking the square root, I don’t truncate to significant digits after each operation. Do you?

Instead, I only truncate to significant digits the numbers that I am reporting as part of my results. Because people would be using it for further mathematical operations, the file you refer to is data rather than results, so it was not truncated to significant digits,.

Now, that may not be your practice, and that’s fine.

But don’t say I’m “guilty” of something just because it doesn’t fit your fancy, particularly for such trivial nonsense. What difference does an extra digit, not in the results but in the data, make in any case? Saying I’m “guilty” is an emotionally loaded word. It implies that I know better and I did it anyways, and it is an ugly and unpleasant accusation.

Now, if you had just posted up the actual data you were concerned about and asked about the number of digits, we could have had a conversation about significance, and whether and when to truncate. Instead, you skipped the questions and went straight to the accusation …

While that is true for pure water at atmospheric pressure, it is not true in the ocean depths. There, the water continues to contract until it freezes.

I showed at the ocean depths the water is not continuing to contract until it freezes. I didn’t believe your claim, I showed why. End, period, full stop. Now you clarified your statement, you changed the conditions, and implicitly say my rejection of your original claim means I appear to reject the revision.

My apologies, for a second time it seems my lack of clarity has led us astray. When I said “It appears you don’t believe my claim”, by “my claim” I meant my claim that sea water continues to get denser up to the freezing point. As far as I can tell, you don’t believe it. I was not referring to the original claim.

I did NOT mean it the way that it appears you’ve taken it. Because of my original lack of clarity, you thought my original claim was that we’d find ice on the ocean floor, and you quite rightly rejected what you thought I meant.

But it appeared to me that you also rejected the idea that sea water continues to become denser right up to freezing. So before we go further, was I wrong in my understanding? Do you accept that sea water is not (as many people thing) at its densest at 4°C, but instead continues to get denser right up until it freezes?

Any attempt to claim that because an 11 or 22 year cycle cannot be determined in SST records is a dead end as to disproving solar influence on climate or saving WUWT from the embarrassment of attacking those who were right at Talkshop.

Konrad, you appear to misunderstand what is going on here. Nir Shaviv made a claim that he could detect the 11-year solar cycle in sea surface temperature records. This was not my claim.

I showed, using his data and his methods, that there is no such significant cycle in the sea surface temperatures. That’s it. End of story. And it’s the same end every time I’ve looked at the story, using the claims of different authors and a whole host of climate datasets.

Since that’s all I’ve done, it’s beyond me why you think this has anything to to do with Tallbloke’s Talkshop (where I’m banned from commenting), or with “saving WUWT” from some imagined embarassment. You seem to think I’m the one making claims. I’m not. I did one simple thing. I showed that Nir Shaviv’s claims that we can detect the solar cycle in the SST to be false. Everything else is just your bizarre imagination.

You did it. You can’t undo it. You just have to wear it.

That is total and complete babble. I did what? I can’t undo what? I just have to wear what? You’re not making sense.

If you incorrectly treat the oceans as a “near blackbody” instead of a UV/SW selective surface, you cannot possibly understand solar influence on climate.

What on earth does that have to do with whether Nir Shaviv’s claims are correct? Are you sure you’re on the right thread?

The effect that needs to be detected is vanishingly small. 0.8C in 150 years. We just don’t have the data. No amount of bleating about 11 year cycles can erase WUWT’s premature “real climate” style dismissal of our variable star’s influence on climate.

Again, Konrad, you need to address this to Nir Shaviv. HE is the one claiming that the effect is not “vanishingly small”, but that it is large and significant. HE is the one claiming that we have enough data to show that it exists. Not me. Nir. All I did was show he was wrong.

And “premature dismissal”? I haven’t dismissed anything. Quite the opposite. I’ve made a very public call for evidence that such an effect as you refer to exists … and nobody, including you, has been able to come up with the evidence. Instead, just like you, all they’ve come up with are excuses as to why the evidence doesn’t exist. Your excuse is that 150 years of data is not enough to detect a strong 11-year cycle, I’ll add it to the excuse list.

And your reason may indeed be the case … but if so, it means the solar effect must be really, really small, because if it were significant, we’d have seen its effects in the climate data after a dozen solar cycles.

So yes, as I remarked above, at this point I’ve seen lots and lots of excuses why we can’t find the effect … what I haven’t seen is any evidence. And whether the idea gets dismissed depends on the evidence, not on me, and not on WUWT.

i quoted the problem i have with this thread, and it wasnt in the post itself, but the comments. but if you must have something from the post it is this-

“What I am saying is that I still haven’t found any convincing sign of the ~11-year sunspot cycle in any climate dataset, nor has anyone pointed out such a dataset. And without that, it’s very hard to believe that even smaller secular variations in solar strength can have a significant effect on the climate.”

you leave this as an implication that the subject is resolved, and of course mosher makes it a statement. but i find this statement just amazing in terms of how complex the system actually is.

Thanks for the quote, mobihci, it makes it clear what you are objecting to.

First, I do not see any implication in my words that the situation is “resolved”. In fact, in the previous paragraph from the one you quoted I said:

Let me be clear about what I am saying and not saying here. I am NOT saying that the sun doesn’t affect the climate.

That specifically means that the question is NOT resolved.

I was just relating my own experience, as I clearly stated, which is that at this point I’ve looked at a very wide range of both climate datasets and claims of 11-year solar cycles in climate datasets, and I have found no such ~11-year periodicities. Just as with Nir Shaviv’s claims, every one of them has evaporated once it is examined closely.

This is important because if the climate system does not respond to the relatively large 11-year solar fluctuations, it’s difficult to believe it would respond to much smaller long-term variations. Why would it respond to small but not large variations?

Does this “resolve” the question? By no means … but it does put an implicit upper limit on the size of any solar effect, which might be described as “dang small” …

Finally, like Konrad, you seem to think that I am trying to prove something. Nothing could be further from the truth. Instead I am trying to falsify something, in the case of this post the claim made by Nir Shaviv of an 11-year cycle being visible in some climate dataset.

As a result, the complexity of the climate you refer to is not an issue. I’m not the one making the claim that a visible 11-year cycle exists in a complex climate system. He is.

I will, however, add your excuse to the list. You claim we can’t see the 11-year solar cycle in climate datasets because the climate is too complex. Got it.

I believe you and the author are deceived by the sunspots because it is not exactly the sunspots that would have the effect on earth. Sunspots are the precursor to CME’s. I strongly believe that if you looked at CME’s during the past 3 cycles which were directed toward earth… you will find your answer.

Are you trying to work yourself up into another stent? I got my own collection, they’re like cats, you just go along minding your business and BAM, you wake up stuck with another one or two.

Seriously, recognizing your stress triggers and learning to blow them off because THEY JUST DON’T MATTER is a valuable survival mechanism.

So where are we at? I say “guilty” like “Hey, you rolled through that stop sign. What if a cop saw you?”

You respond like “How DARE you accuse me of embezzlement and murder! You better have some facts to back that up, you slimy lying bastard!!”

Look, if I had taken your SSN data, straight as you presented them, for my calculations, I would have mixed three different significant positions. You data would have caused my error. You published that as your data, it’s a product supplied by you. On certain numbers you claimed too much significance, your presentation showed significance that wasn’t there.

I understand about not rounding for significance midway through operations. So for a spreadsheet column I’d use a straight calculation, but format to display the values rounded to significant digits. I do the same when programming, that’s my practice.

But your data as presented was a product made by you. There was no notice it was unfinished, no warning there were sharp edges needing smoothing.

SIDC already gave their numbers all rounded to tenths. Your product was labeled as SIDC SSN, but was not all rounded to tenths.

It’s not an accusation, no grand indictment. It’s an observation. At this point it’s like I noted “You dropped the plate,” and you responded “No I didn’t, the dog startled me!” IT DOESN’T MATTER. But someone should still sweep up the shards before somebody gets hurt.

Look, kadaka, I don’t have my blood pressure up about this. But telling a man he is “guilty” of something is indeed an accusation, and not an observation. And in particular, when you fail to quote what you were accusing me of being “guilty” of, it is a very unpleasant accusation. I get accused all the time of all kinds of mopery, and I think I can recognize an accusation by now.

Now, you certainly are free not to believe what I say. If you want to insist it’s all peaches and cream, just an “observation”, go right ahead. I’m just telling you what your actions look and feel like from this side of the screen, and it’s not pleasant. However, you can certainly blow off my observation of how you appear from here, it’s your call.

So before we go further, was I wrong in my understanding? Do you accept that sea water is not (as many people thing) at its densest at 4°C, but instead continues to get denser right up until it freezes?

Alas, dear Willis, I fear this no longer be a right path to be trod.

We both agree that seawater subjected to enough pressure shall yield ice that is dense. We may differ as to whether the ice is still seawater with greater than 0.025 salinity.

We both agree ice is not being formed in the deep ocean.

The basic objectives that arose from my reply to your original statement and also with your reply containing the clarification, are achieved.

Beyond that, we start getting into funny territory. Like how that Fig 5.1 of uncertain provenance does not take pressure into account. But this online freezing point calculator for seawater, with the algorithms referenced, gives me for 35 salinity and pressure of 11,100 (x10 kPa thus 111 MPa, see calculator), which would be seawater at the Challenger Deep depth, a freezing point of -10.281°C, which is very close to what I eyeballed on that log-lin pressure-temperature chart for pure water that I referenced previously.

Pamela Gray says:
June 10, 2014 at 8:16 am
——————————–
Pamela, you are correct. Without the addition of TOA UV measurement all you would be determining is the effect of UV variance on ocean temps below the thermocline, but not whether that UV variance was solar variance or due to internal atmospheric variability such as cloud cover.

In this regard 30 year of satellite records are available but only 10 years of ARGO buoys.

The mechanism for solar influence on ocean temps I am describing is very simple. It is the UV frequencies that vary most between solar cycles. It is the UV frequencies that penetrate deeper than the diurnal overturning layer of the ocean. This allows for a cumulative rather than amplifying effect. While this effect can be demonstrated by empirical experiment, finding it in currently available real world data would be difficult.

It may be possible to use ARGO data (with the readings considered too cold for climastrology replaced) combined with SOHO data and surface meteorological UV-A readings from a island location to detect the mechanism. While ARGO and SOHO are short records, they do span the end of SC23 and the start of SC24.

However while the mechanism may be detectable, records are not long enough to quantify effect on climate.

However, you can certainly blow off my observation of how you appear from here, it’s your call.

I know what I meant. You have your opinion of it. This is the internet, where such has made for nigh-endless multi-venue flame wars. Which result in those involved looking like pretentious self-absorbed idiots.

What I will blow off is any negative feelings I could feel over what I see as your misunderstanding. Water flowing off a duck’s back under the bridge. But I do worry about how you feel about your observation, for your sake.

But the one thing I have learned hard in this life, is people decide to take offense. I gave up on being offended, I’m better for it. A troll will hound you to keep you worked up. The prey don’t play, the troll go away.

Konrad, your contention that UV variation can variably warm oceans at depth is questionable. UV is not a very efficient way of heating ocean water. Besides, depending on conditions, the surface skin reflects not a small amount of UV away, which is why we get more of a sunburn around water than around land. So just how are you proposing that what little UV makes it through our atmosphere can then penetrate the ocean surface skin, make it past the thermocline, and heat things up at a deep level? A quick back of the envelope calculation indicates that there will not be enough energy to do that to any degree that can be measured, even with highly accurate equipment!

Pamela Gray says:
June 10, 2014 at 7:09 pm
——————————–
Pamela,
According to ocean biology papers I have read, UV-A still has the power of 10 w/m2 at 50m depth.
This may vary by about 25% between strong and weak solar cycles. Remember that make believe “CO2 forcing” is only supposed to be around 3 w/m2 per doubling.

Variance in UV heating below the diurnal thermocline has sufficient power to be a viable driver of the very minor 0.8C change in global temperature observed over 150 years.

Willis,
I’ve looked at it as well – sorry you didn’t find squat. There clearly is a rise in air temperature for a few days after flares hit earth. You can assume it has no effect on sea surface temperature….. The exact meaning of surface varies according to the measurement method used, it can be between 1 millimetre (0.04 in) and 20 metres (70 ft) below the sea surface.

have you ever swam in an outdoor pool a few weeks after it was opened. You swim around and there are these very warm spots…. and very cold spots. why do you suppose that is? Why wouldn’t the heat evenly diffuse across the pool of water? because it takes work to dispense it. These spots are there even when the pool filter is running.

In other words…. depending on the location of the measurement, the orientation of the earth as the flare hits it…. perhaps the position of the moon as well….. cloud cover on a given day….. (it boils down between the amount of sea surface exposure) There are too many variables within the data that need to be checked to show you the correlation.

Then, of course…. there is downward currents in the ocean. there is miles of water below…. its much more than surface temperature.

When you put a global pot of water in a grand solar maximum…. it takes time to heat it up.

have you ever swam in an outdoor pool a few weeks after it was opened. You swim around and there are these very warm spots…. and very cold spots. why do you suppose that is? Why wouldn’t the heat evenly diffuse across the pool of water?

Because of the temperature sinks. You talk of outdoor pools that were opened, sounds like a public pool. Those are normally made of concrete and in-ground. Now about 10 feet or so into the ground worldwide, you find year-long temps in the fifties Fahrenheit.

So the bottom of the pool is a temperature sink, at the temps that nearly-naked humans like to swim in there would be warmth lost to the ground underneath. Heat would also be lost into the walls, especially corners.

The mass of water under the surface, above the bottom, and away from the walls sees not that much circulation, with the inlets and outlets at the perimeter and the amount of force they can supply minimized to avoid damaging the fleshbags. So that’s the major warmer spot, enough under the surface that the effect of evaporative cooling is negligible.

When you put a global pot of water in a grand solar maximum…. it takes time to heat it up.

Especially when energy and temperature differences are so little. If I have a ten gallons in a stainless steel pot on a burner getting enough heat to maintain 73.1°F, then add just enough more that I should get 73.2°, I expect it to take quite some time.

Willis,
I’ve looked at it as well – sorry you didn’t find squat. There clearly is a rise in air temperature for a few days after flares hit earth. You can assume it has no effect on sea surface temperature….. The exact meaning of surface varies according to the measurement method used, it can be between 1 millimetre (0.04 in) and 20 metres (70 ft) below the sea surface.

Without any data, quotations, code, or documents, that means absolutely nothing.

have you ever swam in an outdoor pool a few weeks after it was opened. You swim around and there are these very warm spots…. and very cold spots. why do you suppose that is? Why wouldn’t the heat evenly diffuse across the pool of water? because it takes work to dispense it. These spots are there even when the pool filter is running.

Unverifiable anecdote.

In other words…. depending on the location of the measurement, the orientation of the earth as the flare hits it…. perhaps the position of the moon as well….. cloud cover on a given day….. (it boils down between the amount of sea surface exposure) There are too many variables within the data that need to be checked to show you the correlation.

Say what? You claim that there is “clearly a rise in air temperature for a few days after [solar] flares hit the earth” … then you say that the data has “too many variables” to show the correlation.

If there are too many variables … then how do you know there is a rise in air temperature?

Then, of course…. there is downward currents in the ocean. there is miles of water below…. its much more than surface temperature.

True … and???

When you put a global pot of water in a grand solar maximum…. it takes time to heat it up.

If that is true, then how do you explain the fact that the sun can heat up the ocean surface a couple of degrees in a single day?

Your satellite surface temperature data IS DIDDLY SQUAT.

Thanks for sharing this exciting insightful analysis … but again, since you have provided nothing but your mouth to back it up, I fear your opinion is of little interest to the world of science.

have you ever swam in an outdoor pool a few weeks after it was opened. You swim around and there are these very warm spots…. and very cold spots. why do you suppose that is? Why wouldn’t the heat evenly diffuse across the pool of water?

Because of the temperature sinks. … [good stuff snipped]

When you put a global pot of water in a grand solar maximum…. it takes time to heat it up.

Especially when energy and temperature differences are so little. If I have a ten gallons in a stainless steel pot on a burner getting enough heat to maintain 73.1°F, then add just enough more that I should get 73.2°, I expect it to take quite some time.

KD, people are using the slow temperature response you are looking at to claim that the 11-year cycle would not be visible. Is that your point here as well? Because I assure you, if you “add just enough more [heat] that should get 73.2°”, it will indeed take “quite some time” to warm up your pot of water.

However, you can certainly blow off my observation of how you appear from here, it’s your call.

I know what I meant. You have your opinion of it. This is the internet, where such has made for nigh-endless multi-venue flame wars. Which result in those involved looking like pretentious self-absorbed idiots.

What I will blow off is any negative feelings I could feel over what I see as your misunderstanding. Water flowing off a duck’s back under the bridge. But I do worry about how you feel about your observation, for your sake.

But the one thing I have learned hard in this life, is people decide to take offense. I gave up on being offended, I’m better for it. A troll will hound you to keep you worked up. The prey don’t play, the troll go away.

Your decision, your life. Hope it’s a good one.

KD, if you were a troll, I’d definitely do that. But you’re not. You are someone who is obviously serious and smart and as far from a troll as I can imagine.

Now, if you wish to continue to go around accusing people of being guilty without presenting any evidence, and then being all amazed and noble and condescending when they get upset, that’s your call.

But it has nothing to do with “my decision, my life”. I’m no different from anyone in this regard. When someone whose opinion I value makes an evidence-free attack on me, I protest, just as you or anyone else would.

So if (as it seems) you don’t think you did anything wrong … well, I’m not gonna be the last guy who surprises you by spitting in your face when you make your next mistake in that regard.

And trying to blow it off by saying that you know what you meant? I’m sure you do know what you meant, KD. I’m just trying to get you to realize that what you meant doesn’t make a damn bit of difference in the real world, it’s just more good intentions paving the road to hell.

What counts is the effect your words have … and telling a man he’s “guilty” of something without providing evidence is almost guaranteed to have a bad effect. Why is that so hard for you to understand?

Yes, your intentions were good, I accept that … but here’s a tip about how the world works. Regardless of your good intentions, when you falsely claim a man is “guilty” of something without providing evidence to back up your claim, the person you accuse is likely to get upset, and you/re likely to get your face slapped … that’s just how it is.

Finally, when you have offended a man, claiming that you personally “gave up on being offended” doesn’t help your case, it just makes you sound like a prissy jerk. I don’t give a fig how noble you are —when you go around offending people, nobody cares if you don’t get offended. Yeah, OK, you’re enlightened and wonderful and free of earthly cares, you don’t get offended like everyone else does, that’s great … but why do you think that gives you license to offend people?

Has this study been commented upon in this post or prior on the 11-year cycle? I pasted it into another comments section previously. Could be the much discussed here UV variation effect on both stratospheric ozone & sea surface:

An international team of scientists led by the National Center for Atmospheric Research (NCAR) used more than a century of weather observations and three powerful computer models (I know, I know!) to tackle this question.

The answer, the new study finds, has to do with the Sun’s impact on two seemingly unrelated regions: water in the tropical Pacific Ocean and air in the stratosphere, the layer of the atmosphere that runs from around 6 miles (10 km) above Earth’s surface to about 31 miles (50 km).

The study found that chemicals in the stratosphere and sea surface temperatures in the Pacific Ocean respond during solar maximum in a way that amplifies the sun’s influence on some aspects of air movement. This can intensify winds and rainfall, change sea surface temperatures and cloud cover over certain tropical and subtropical regions, and ultimately influence global weather.

“The sun, the stratosphere, and the oceans are connected in ways that can influence events such as winter rainfall in North America,” said lead author of the study, Gerald Meehl of NCAR. “Understanding the role of the solar cycle can provide added insight as scientists work toward predicting regional weather patterns for the next couple of decades.”

The findings are detailed in the Aug. 28 issue of the journal Science.

Has this study been commented upon in this post or prior on the 11-year cycle? I pasted it into another comments section previously. Could be the much discussed here UV variation effect on both stratospheric ozone & sea surface:

Thanks, milodon. Hadn’t looked at it, and it’s by the the congenital modeler Gerald Meehl of NCAR, not a good sign. At NCAR, amazingly, they actually believe their models … go figure. I also note it was published in 2009 and sank without a trace, probably deservedly so, but hang on while I read it …

…

OK, I just went and took a look. No data were harmed in the writing of the study, it is nothing more than the trivial and uninformative results of three climate models … and from the graphics in the paper, they are crappy climate models.

In any case, I have shown that the current generation of climate models are robotic linear machines, which do nothing more than spit out a lagged and scaled version of the inputs.

As a result, when they are forced with solar changes, they output a lagged linear response to those solar changes … are we surprised?

However, anyone who thinks that says anything about the real world needs to reconsider the linear nature of the models and the non-linear nature of the planet. You can find such solar-model result correlations in just about every model output … but to date, we have no record of such solar effects in the real world, despite my repeated call for contestants.

The other study sank out of sight, IMO, because it challenges the GHG orthodoxy. The high priests at NCAR practice figurative human sacrifice of non-conformist heretics. But I have to grant that Meehl is indeed a modeler. I can’t evaluate whether the three models his team used are better or worse than most GCMs.

Here’s the abstract:

One of the mysteries regarding Earth’s climate system response to variations in solar output is
how the relatively small fluctuations of the 11-year solar cycle can produce the magnitude
of the observed climate signals in the tropical Pacific associated with such solar variability.
Two mechanisms, the top-down stratospheric response of ozone to fluctuations of shortwave
solar forcing and the bottom-up coupled ocean-atmosphere surface response, are included in
versions of three global climate models, with either mechanism acting alone or both acting
together. We show that the two mechanisms act together to enhance the climatological
off-equatorial tropical precipitation maxima in the Pacific, lower the eastern equatorial
Pacific sea surface temperatures during peaks in the 11-year solar cycle, and reduce
low-latitude clouds to amplify the solar forcing at the surface.

Did you put out a data file for public view with numbers having more significance than warranted for a final version? Yes.

You are objecting to my saying you are “guilty” of “claiming” too much significance by that act and have demanded an apology.

The wording obviously means much more to you than it does me. Fine, I apologize.

You say I have done this act without presenting any evidence. First time, for an offhand remark, I thought the evidence was obvious from context. You demanded the evidence. So I provided it explicitly.

Will you admit those numbers were shown with too much significance? Or is it true that what I think you’re saying is correct, that it’s allowable as those are intermediate results, despite there being no identification as such? Or is the truth something else?

Really? That’s your idea of a scientific judgement, that the results “look pretty good”? Me, I think they look like what I’d expect from badly munged data. If you truly think their analysis is good, how about you repeat it without their bogus boxcar filter and give us the results?

Did you put out a data file for public view with numbers having more significance than warranted for a final version? Yes.

NO. I put out what I clearly identified as data which was the result of interim calculation, which I specified. Since it was to be used for further calculations, I did not round it off. I see you don’t like that, and it is a matter on which reasonable men can disagree … but it doesn’t make me “guilty” of “claiming” anything.

You are objecting to my saying you are “guilty” of “claiming” too much significance by that act and have demanded an apology.

The wording obviously means much more to you than it does me. Fine, I apologize.

Great. Then we’re done with that part of it.

The part I’m not sure you get yet is that telling a man that he is guilty, no matter of what, is a very strong accusation. You are saying that he has knowingly done something that is wrong. I mean, we don’t say a man is “guilty” if he makes a mistake in adding up some numbers, we say he made an error. And you could have said that about my actions, that I’d made an error … but you didn’t.

You also don’t seem to see that since assigning guilt is such a strong accusation, it should NEVER be made without accompanying evidence. Doing that totally deceives the casual reader into thinking you are possibly right, it leaves your accusation unsupported, and it prevents the accused from answering the accusation.

As a result, when you claim a man is “guilty” of something, particularly without specifying what the hell you’re referring to, you’re damned right it will “mean much more to him than to you”. You’re not the one being accused, and more to the point, you obviously toss off such an unpleasant accusation without much thought. It’s clearly nothing to you to make such an uncited, unexplained claim about someones guilt, and in fact you defend it.

However, from this side of the screen, you are claiming that I am a guilty man without saying what you think I’m guilty of. And yes, KD, that obviously means more to me than to you … and that’s exactly the problem. Hopefully, in future you will take your own words as seriously as others out here take them, and lay off the accusations. My own goal in that regard is to never ascribe to bad intentions what is explainable by error and ignorance … and an accusation of guilt is an accusation of conscious wrongdoing.

In any case, I’ll give you the last word on this, I think I’ve said what I have to say.

Thanks, milodon. You may indeed be right … but where is your evidence? I demonstrated the damage that a 10-year boxcar filter did to sunspot data here. If you think it “isn’t necessarily bogus”, please present your worked example. I’ve provided mine …

The most relevant evidence, of course, would be what I requested above, viz:

If you truly think [the Christensen/Lassen] analysis is good, how about you repeat it without their bogus boxcar filter and give us the results?

Me, I generally don’t go drilling what I think will be a dry well … but if you want to give us your analysis of the paper that you are saying is evidence, I’m all ears.

It has recently been suggested that the solar irradiance has varied in phase with the 80- to 90-year period represented by the envelope of the 11-year sunspot cycle and that this variation is causing a significant part of the changes in the global temperature. This interpretation has been criticized for statistical reasons and because there are no observations that indicate significant changes in the solar irradiance. A set of data that supports the suggestion of a direct influence of solar activity on global climate is the variation of the solar cycle length. This record closely matches the long-term variations of the Northern Hemisphere land air temperature during the past 130 years.

As I have shown, there is no “80- to 90-year period” in the envelope of the solar data. So they start with a false premise, and their paper is designed to uphold a non-existent relationship.

In addition, they use the unadjusted Zurich sunspot numbers, which contain a spurious trend.

They go on to say:

A different solar parameter showing
long-term changes is the length of the
sunspot cycle. This parameter is known to
vary with solar activity so that high activity
implies short solar cycles whereas long
solar cycles are characteristic for low activity
levels of the sun. Gleissberg (12) demonstrated
that the variation occurred in a
systematic manner with a long-term periodicity
of 80 to 90 years, now known as the
Gleissberg period.

Unfortunately, Gleissberg showed no such thing, and no such 80-90 year cycle can be demonstrated in the data, as I’ve discussed here and here. However, that didn’t stop C&L from using Gleissbergs flawed method, vis:

We determined the length of the sunspot
cycle using epochs of maxima and minima
found by the secular smoothing procedure
introduced by Gleissberg (12) (Fig. 2). This
procedure corresponds to the application of
a low-pass filter with coefficients 1, 2, 2, 2, 1
to the series of individual sunspot maximum
and minimum epochs. This particular filter
was selected because it has been generally
used in the determination of long-term
trends in solar activity, but the use of a
different filter would not change the results
significantly, as long as the short-term variations
related to the 1 1-year cycle and shorter
periods are removed. For the last two
extrema, the available data do not allow full
smoothing. Therefore, we filtered the second
to last extrema by estimating the next
extremum (because this is included in the
filtering with a weight of one-eighth only);
the last extrema express the unfiltered epochs.

Now, there is a leetle teeny tiny problem:

The temperature record is only available
for the last 130 years, which is about 1.5
cycles of a possible 80- to 90 year oscillation.

But fear not, the authors have a solution in hand:

The official Zurich sunspot number, however,
extends back to 1715, and it is therefore
possible to calculate the smoothed sunspot
cycle length from 1740. This in principle
allows a comparison between the length of
the solar cycle and a parameter that could
be regarded as a reasonable estimate of the
global temperature. One parameter covering
this long time period is an index
of the North Atlantic sea ice, which is
known to show similar long-term variations.
Although the individually measured
extensions of sea ice suffer from a number
of different influences and probably cannot
be used directly as a temperature index,
it seems reasonable that the absence of
considerable amounts of ice would be associated
with relatively high global, or at
least hemispheric, average temperatures.We therefore compared (Fig. 3) the
smoothed sunspot cycle lengths and a 22-
year running mean of the extent of sea ice
around Iceland (13, 14).

This is just too good. They are taking sunspot data, and extracting from that the alternating lengths of the maxima and minima of the cycles. Then they are running a 1-2-2-2-1 filter on those values as though they were evenly spaced in time, which they are definitely not.

Then they are comparing whatever they got from those curious machinations to a 22-year running mean of sea ice extent around Iceland since 1740, and declaring victory.

Of course, they have not bothered with such mundane things as actually figuring out the correlation of the smoothed Iceland sea ice and the munged cycle length data. No mention of that at all. And since they present no correlation figures, of course we also have no accompanying significance figures … here’s their money graph:

ORIGINAL CAPTIONFig. 3. (Top) 22-year running mean of the amount of sea ice around Iceland from 1740 to 1970 during summer months (represented by the number of weeks when ice was observed). (Bottom) Smoothed sunspot cycle lengths from 1740 to 1970 (left-hand scale) and Northern Hemisphere mean temperature (right-hand scale).

Bizarrely, they appear to be using a combination of alternating minima-minima cycle lengths and maxima-to-maxima cycle lengths …

Seriously, that’s all their evidence, Figure 3. No correlation analysis, no r2, no consideration of the foolishness of simply eyeballing two highly and curiously smoothed datasets …

I suppose I shouldn’t be surprised by Science magazine publishing this kind of pseudo-scientrash, but I always am … I mean, no statistical analysis of the results at all? Really? How does that pass the peer review?

Regards,

w.

PS—Did you notice the oddity about their Figure 3? What they call the “Northern Hemisphere mean temperature” is nothing of the sort. Instead, it is the NH mean temperature sampled only at the time of the sunspot maximum and minima … cute. Real cute. They’ve reduced the 140-year NH temperature dataset to only 19 data points. Of course, they never touch the question of what that does to the significance of their results … and wisely so …

OK, no more pulling you back in, Don Willis. (FWIW, Puzo based the Godfather on the Borgias. Cesare was reputed to have killed his brother Giovanni, but while their father Rodrigo, aka Pope Alexander VI, still lived.)

Konrad, I would imagine that intrinsic factors play a much larger role in UV variation in the oceans than solar affects. Solar affects are likely to be buried in the noise. UV at Earth’s surface is VERY noisy and is even noisier at depth in the oceans. I seriously doubt a mathematically measurable affect, and certainly not one that would show up in SST measures, which are themselves highly variable and under-sampled. I also doubt your quoted heating calculation. See the following:

Ultraviolet B (UV-B) radiation reaches different depths in ocean water depending on water chemistry, the density of phytoplankton, and the presence of sediment and other particulates. The map above indicates the average depth UV-B penetrates into ocean water. At the depth indicated, only 10 percent of the UV-B radiation that was present at the water’s surface remains. The rest was absorbed or scattered back towards the ocean surface.

OK, no more pulling you back in, Don Willis. (FWIW, Puzo based the Godfather on the Borgias. Cesare was reputed to have killed his brother Giovanni, but while their father Rodrigo, aka Pope Alexander VI, still lived.)

But it seems you might have enjoyed the trip down memory lane.

Indeed, milodon, and thanks for pulling me back for that last bit of fun. I was amazed when I finally read that study in detail just how abysmally bad it was …

… The other study sank out of sight, IMO, because it challenges the GHG orthodoxy. The high priests at NCAR practice figurative human sacrifice of non-conformist heretics. But I have to grant that Meehl is indeed a modeler. I can’t evaluate whether the three models his team used are better or worse than most GCMs.

I guess my point wasn’t clear, usually that’s my fault. The issue is not whether the models are “better or worse”. The issue is that they are all linear, in that their outputs are nothing but lagged linear transforms of the forcing.

As a result, we can almost guarantee that if there is solar forcing in the model inputs, it will appear in the outputs. And this simple fact means that climate models are absolutely useless in determining whether the solar variations have any affect on the climate … because by and large, the models will say yes.

But unfortunately, this is means absolutely nothing about the real world. It just means that the models are boringly and robotically linear.

By definition, the sample cross-correlation function is, at each lag m, the
average covariance product x(n)*y(n+m), normalized by the product of the
sample RMS-values of x and y. The latter do not distinguish between
signal, interferring components in other frequency bands, and noise; their
variances simply add together. Thus when narrow-band signals such as SSN
are cross-correlated with a wide-band signal such as SST, only the fraction
of total variance of the wide-band signal in the narrow frequency range of the former can contribute systematically to the cross-correlation.

While ~80% of SSN variance is confined to spectral bands ~11yrs in period,
only ~20% of surface temperature variance is found there, with multidecadal
oscillations accounting typically for more than half. That’s why the
cross-correlation is down by a factor of ~4 from what it would be if
temperature variations were likewise narrow-band and coherent. It is sheer folly to conclude on the basis of confidence intervals appropriate only to
TIME-INVARIANT quantities that the LAGGING relationship between SST and SSN,
plainly evident in the sample ccf in Figure 3, is statistically
insignificant.

This signal processing engineer would have arrived at the same conclusion – that if you put a high frequency signal (11 year period) into a low-pass filter (general thermal inertia of ocean) you don’t want to brag about what comes through the noise, particularly as magnified by an “analysis filter”.

Of interest was figure 3.(d), this shows UV-A still having a power of ~10 w/m2 at 50m, which is well below the thermocline.

I believe that UV the heating below the thermocline should be measurable with current instruments. All I am pointing out is that this will be one of the “many plugs” Jack Eddy mentioned with regard to solar influence on climate, however the record length of current instruments is too short to quantify effect on global climate variation. Sadly this is what wills calls an “excuse” as to why I cannot show 11 year cycles I clearly claimed would not be visible in SST records above the thermocline.

But then I am still wondering what Willis’ “excuse” is for holding the lukewarmer line. After all he lost the “does DWLWIR heat the oceans” argument to a roll of microwave-safe cling wrap way back in 2011..;-)

konrad says
Well, UV-A heating below the diurnal thermocline is a very plausible mechanism that also accounts for lag.
henry says
did you figure out already what the next 44 – 46 years of this graph looks like?

@Konrad
there is some variation within TSI, mainly to do with the UV (C). It appears (to me) that as the solar polar fields are weakening, more energetic particles are able to escape from the sun to form more ozone, peroxides and nitrogenous oxides at the TOA.
In turn, these substances deflect more sunlight to space when there is more of it. So, ironically, when the sun is brighter, earth will get cooler. This is a defense system that earth has in place to protect us from harmful UV (C).

Konrad, using your link, I see that photosynthetically active radiation (visible light range 400-700nm) has far more W/m2 than UVa (315-400nm) at the same ocean depth. In terms of heating capacity, the longer the wavelength, the more energy it carries for the purpose of heating. Thus, any temperature variation of the sort scientists have measured would be from visible light and would bury any temperature variation from UVa. Therefore, UVa cannot be the source of short or long term measurable temperature trends in the oceans. If anything, it would be from variation in visible light the moment it breaks the ocean surface skin combined with absorption in the water column, leaving left over heat to drive trends

Sadly, it’s a typical 1sky1 comment. Big on claiming how much he knows, full of opinions, and totally lacking in any kind of citation, worked examples, quotations, or facts.

This signal processing engineer would have arrived at the same conclusion – that if you put a high frequency signal (11 year period) into a low-pass filter (general thermal inertia of ocean) you don’t want to brag about what comes through the noise, particularly as magnified by an “analysis filter”.

Since you have given us absolutely no evidence that the ocean is a “low-pass filter” for temperature, and since the ocean responds to solar variations on a daily and an annual basis, and many folks claim that it responds on an 80-100 year basis, it doesn’t take a signal processing engineer to know that a low-pass filter wouldn’t do anything at all like you claim. Instead, a low-pass filter would also cut out the annual and daily cycles. You do understand what “low-pass” means, right?

Of course, you don’t provide any more facts, citations, worked examples, or evidence than 1sky1 did … and understandably so, given the ludicrous nature of your claims.

If you and 1sky1 are an example of the signal processing engineers these days, god help us all. Where I come from, signal engineers actually demonstrate that a given low-pass filter exists before they start bothering other people with their theories about the putative filter … and they provide data, facts, observations, and citations to back up their theories.

So you and 1sky1 showing up with nothing in hand but your Johnsons and your big mouths doesn’t impress me in the slightest. I know real signal engineers.

Finally, you need to hone your reading skills. I said:

Konrad, you appear to misunderstand what is going on here. Nir Shaviv made a claim that he could detect the 11-year solar cycle in sea surface temperature records. This was not my claim.

I showed, using his data and his methods, that there is no such significant cycle in the sea surface temperatures. That’s it. End of story. And it’s the same end every time I’ve looked at the story, using the claims of different authors and a whole host of climate datasets.

Since I used his method, if you have problems with the method, you’re talking to the wrong guy. I didn’t pick the method, Nir Shaviv did. Go whine to him, maybe you can convince him you know your tailpipe from a hole in the ground. Me, not so much …

Pamela Gray says:
June 11, 2014 at 2:23 pm
——————————–
But what if we were to forget pointlessly stabbing the 11 year straw man?

Konrad, I’ve been through this before, but you still don’t seem to understand. I’m not the one who is claiming that there is an 11-year solar cycle in various climate datasets. That would be Nir Shaviv and a host of others. I am using their own data and methods to show that they are wrong.

So I’ll thank you to take your unpleasant “straw man” claims elsewhere. I’m not creating a straw man. I am falsifying scientific claims that have wrongly gained currency in the scientific literature.

And falsifying such claims is a legitimate and valuable scientific enterprise. If you don’t like it, then you’re not actually a scientist, because that’s what scientists do.

And if so, if you think such falsification is “stabbing the 11-year straw man”, then what are you doing here? Why aren’t you off where people are doing something important, if this is just straw man stabbing? Truly, if you believe that, take it somewhere else … because whining about straw men here is going to go nowhere. I’m clear on what I’m doing.

Willis Eschenbach says:
June 12, 2014 at 11:28 am
Big on claiming how much he knows, full of opinions, and totally lacking in any kind of citation, worked examples, quotations, or facts.
——————————————————-

If you have a process driven by several parameters plus noise, correlation values must be generally lower than in the single parameter case.

take, for example a function

f = sin(t) + 2 cos(t) + noise(t)

where f is driven by 2 influences and we are correlating only with the a representation of the first sin(t). The correlation value then can never be higher than 1/3. Accordingly significance values assuming a single parameter process are meaningless.

The other processes have to be either reduced before correlating (removal of trends, removal of known drivers such as ocean cycles, volcanoes, etc.) or included in a multi-parameter estimation.

“In the ocean, seawater acts as a form of natural low-pass filter, attenuating high-frequency wave or acoustic energy at a much higher rate than low-frequency energy.”

Granted, this refers to sound rather than EM radiation propagation, but IMO tends to support 1sky1′s case.

Dear heavens, don’t go to the dark side, think about what you are saying.

You are saying that something which acts as a low-pass filter for sound at a frequency of say 40-4000 hz will therefore also serve as a low-pass filter for a thermal variation at a frequency of 0.0000000029 hz … do you really believe that? And if so, what is the theoretical framework connecting the two phenomena?

What’s next? Are you going to claim that the low-pass filter in my radio’s equalizer, which like the ocean acts as a low-pass filter for sound at a frequency of say 40-4000 hz, will therefore also serve to attenuate 11-year thermal variations? Because that makes about as much sense.

And 1sky1 doesn’t have a “case”. He has a trivially tossed-off claim that the ocean selectively suppresses 11-year cycles but doesn’t affect 1-day or 1-year or 80-year cycles. If he had advanced even one tiny scrap of evidence in support of that he’d have a case, albeit a weak one.

But right now, all 1sky1 has is a mouth. Oh, and his Johnson in his hand. And to date, despite lots and lots of opinions, and claims as to how brilliant he is and how stupid we all are to not recognize it … a big mouth with nothing in hand but his Johnson is all he’s ever displayed here. Perhaps he’s allergic to data and observations, I don’t know.

My favorite time was when I asked him to actually run the analysis he was so strongly urging on me, so I could see how to do it right.

Paraphrasing, of course, he claimed I was trying to get him to perform his valuable work and use his stupendous intellect for free, and that if I wanted him to actually back up his words with observations or data or actual analyses, I’d have to pay him to do so … my advice is avoid him, milodon, he’s bad news.

I haven’t read his comments often enough to know 1sky1’s history, which I have no reason to think you have not accurately characterized. Maybe “case” was too strong a term, but I don’t see a reason to reject low pass filtering effect out of hand. Climate science still lacks basic research.

However, comparing a radio equalizer to seawater isn’t a convincing comparison IMO. Obviously there’s a big physical & energetic difference between the filtering of sound waves & photons, but the same applies to the media of seawater & electrical device parts.

An experiment with optical back-scattering frequencies on seawater with added particles also showed a low-pass filter-like response.

Water molecules absorb in the UV, so they “filter” at the high end of the EM energy spectrum. The effect of seawater of course depends upon its surface & subsurface conditions & the dissolved materials it contains.

I may well have misunderstood. I thought you were saying that you couldn’t find any 11 year cycle, and I thought 1sky1 had made a coherent comment (hence my characterization as a “carefully formulated remark”) in support of your findings, if not your methods. I in turn saw his analysis as consistent with a filtering viewpoint, also supporting your finding if not your methods.

If you have a process driven by several parameters plus noise, correlation values must be generally lower than in the single parameter case.

take, for example a function

f = sin(t) + 2 cos(t) + noise(t)

where f is driven by 2 influences and we are correlating only with the a representation of the first sin(t). The correlation value then can never be higher than 1/3. Accordingly significance values assuming a single parameter process are meaningless.

The other processes have to be either reduced before correlating (removal of trends, removal of known drivers such as ocean cycles, volcanoes, etc.) or included in a multi-parameter estimation.

Manfred, I guess I have to say it a number of times before people get it. I used Nir Shaviv’s methods and data. If you have a problem with the method, you are in the wrong bailiwick. I didn’t pick the method, so you should go complain to the man who did.

I don’t care in the slightest if folks don’t like my methods. When I looked at Gleissberg’s claims, I used Gleissberg’s methods. When I looked at Holgate’s claims I used his methods. When I looked at the Parana River claims, I used their methods. And in this analysis I’ve used Shaviv’s methods.

So if you think my methods are wrong, Manfred, I’ve already invited you and 1sky1 and bernie and all the rest to use your own oh-so-brilliant methods that avoid all of the obvious and stupid mistakes you claim I’m making, and to grab a climate dataset and apply your whiz-bang math and show us the claimed 11-year cycle.

Unfortunately … y’all either can’t read, or you would rather try to impress me with theory. I don’t care about the theories. Oh, they’re interesting to pass the time with, I don’t mean they’re a bad thing, and I often learn from them either what to do or what to avoid.

But all of that is miles away from the subject of this post. Either someone can demonstrate the 11-year cycle exists in a given dataset as demonstrated by a given method, or they can’t.

And to date, no one has demonstrated such a dataset and method, include all of you big-brained theorists out there telling me where my method is all screwed up. You’re talking to the wrong guy. Ring up Nir Shaviv and get on his case, it’s his method.

That said, your underlying claim about significance in multi-variable process is correct. However, without having any idea of either the size or the nature of any possible confounding factors it’s very difficult to know where to go with that claim in actual practice.

However, you’ve picked a very poor example. Ignoring the error term, your equation is

f = sin(t) + 2 cos(t)

Subtracting and squaring, we get

(f-sin(t))2 = 4 cos(t)2

Using the identity that

sin2 + cos2 = 1,

the equation reduces to

(f-sin(t))2 = 4 (1 – sin(t)^2)

so we can eliminate one of the two variables. Just sayin’ … pick another example.

In the real world, however, this issue is usually dealt with through some kind of filtering, whether Fourier, or “folding” as Shaviv did, or one of many, many other methods, some devised for one peculiar unique situation and others of general utility, to clarify the muddy waters so that we are only looking at the signals of interest. The purpose of these filters is to attenuate the other confounding factors so that they don’t significantly affect the outcome, and in general, they work surprisingly well.

I haven’t read his comments often enough to know 1sky1′s history, which I have no reason to think you have not accurately characterized. Maybe “case” was too strong a term, but I don’t see a reason to reject low pass filtering effect out of hand.

I reject it out of hand because there is no apparent filtering out of daily or annual signals, which is the definition of a low-pass filter—it attenuates the daily and annual higher-frequency signals.

Willis said June 12, 2014 at 1:31 pm in part in reply to milodonharlani :
“…I reject it out of hand because there is no apparent filtering out of daily or annual signals, which is the definition of a low-pass filter—it attenuates the daily and annual higher-frequency signals….”

A length of copper wire is a low-pass filter if your signal source is a flashlight. It’s the same with any frequency as high as 1/11-years striking the ocean. You could likely DETECT the attempted invasion, the input, AT THE INTERFACE (the light beam striking the metal, or a daily or yearly temperature change at a suitably limited ocean surface layer). But that’s low-pass – IF you have correctly located the filter output point, the signal does not get through.

Maybe in coming years Argo floats will provide observational data adequate for detecting an 11-year signal in ocean temperature, to go with the rainfall data for which the CACA Team has been more willing to allow a solar effect, or possibly so far as to admit the possibility of regional warming effects.

Still, solar activity & earth’s modulation of irradiance into insolation have been shown to the satisfaction of most since the 1970s to play an important role in climate on longer time frames.

I may well have misunderstood. I thought you were saying that you couldn’t find any 11 year cycle, and I thought 1sky1 had made a coherent comment (hence my characterization as a “carefully formulated remark”) in support of your findings, if not your methods.

Dang, did I misread his comment? Hang on while I find it again …

While ~80% of SSN variance is confined to spectral bands ~11yrs in period,
only ~20% of surface temperature variance is found there, with multidecadal
oscillations accounting typically for more than half. That’s why the
cross-correlation is down by a factor of ~4 from what it would be if
temperature variations were likewise narrow-band and coherent. It is sheer folly to conclude on the basis of confidence intervals appropriate only to
TIME-INVARIANT quantities that the LAGGING relationship between SST and SSN,
plainly evident in the sample ccf in Figure 3, is statistically
insignificant.

Bernie, please, show me one fact in there. All we have are claims. There’s no analysis. There’s no data. There’s no examples. It’s 1sky1 once again making unsubstantiated claims. Some sub-set of those claims are likely right, but without a scrap of data or citation, there’s no way to know.

In addition, the maximum correlation in Figure 3 is not lagged at all, it’s at t = 0. What “LAGGING relationship” is he claiming when max correlation is with no lag at all?

Also, as is totally typical with 1sky1’s posts, he never, ever tells us or shows us how it should be done. Re-read his comment. It is confined entirely to how I’m doing things terribly wrong. Well, maybe so … but until a man can demonstrate that he can do it right, I must confess that I pay little attention to his claims that I’m doing it wrong. Particularly in this case since I’ve asked and he’s refused to show us how to do it right, more than once. He’s had a variety of excuses, including that I’d have to pay him to do such a complex analysis …

It’s actually quite curious. It seems he’ll tell me that I’m doing it wrong for free, but he wants money to tell me how to do it right.

In any case, his point seems to be that because the power spectra of sunspots and sea surface temperature don’t overlap much, it’s inappropriate to use standard significance levels for correlation.

To see why this is incorrect, let’s suppose the opposite were true and their spectra DID mostly overlap. In order to do that, the temperature would have to show a peak in the spectrum around 11 years … and of course, at that point we’d get high correlation, and from that we’d conclude that the sun was indeed affecting the temperature.

And this means that the fact that the spectra don’t overlap much is a SYMPTOM of the lack of a physical effect. If there were such a physical effect, if the 11-year solar cycles actually were affecting the SST, the spectra would overlap much more, the temperature data would be more narrow-band.

In any case, Bernie, if 1sky1 wants to show us the right and proper way to deal with what he calls the “LAGGING correlation” in Figure 3, which isn’t lagging, I’m all ears. I’ve made the offer up top, use any methods that you might think are appropriate. So until he stops his incessant whining about how I’m doing it wrong, wrong, wrong and actually shows us how to do it right, I’ll continue to mostly just ignore his postings.

Maybe in coming years Argo floats will provide observational data adequate for detecting an 11-year signal in ocean temperature, to go with the rainfall data for which the CACA Team has been more willing to allow a solar effect, or possibly so far as to admit the possibility of regional warming effects.

Whoa, back up there. What rainfall data is supposed to show an 11-year cycle?

Tree rings, ice core data & radionuclides. Dendro isn’t much use for reconstructing T, but better at precip. Papers containing the relevant data have been commented or blogged upon here over the years.

Here’s a recent one:

A 3,500-year tree-ring record of annual precipitation on the northeastern Tibetan Plateau

While not specifically dealing with the average 11-year cycle, it does provide annual data from which the signal is recoverable. The paper is more oriented toward trying to associate precip with T than with short-term solar activity.

“Abstract
Spectral and wavelet analysis were performed on a tree ring width time series obtained
from a 2500 yr old cypress tree (Fitzroya cupressoides) from Costa del Osorno, Chile.
The periods for analysis were selected at 95% confidence level. Both periodicities
characteristic 5 of solar activity and climatic variations were found in this tree ring width
series. The 11 and 22 years solar cycle periods were present in tree ring data with a
confidence level above 98%. This indicates the solar modulation of climatic variations
is being recorded by the tree ring grown. However wavelet analysis shows that these
are present only sparsely. Short-term variations, between 2–5 years, are also present
10 in tree ring data, and are shown by wavelet maps to be a more permanent characteristic.
This time scale is a signature of ENSO events. Long-term variations, above 200
years, are also present in tree ring data. The spectral analysis performed in this work
shows that this species has the ability to record solar-ENSO variations that seems to be
affecting the local environment of tree growth, and also that this region was influenced
15 by ENSO events at least in the past 2500 yr interval covered by this study.”

We’re lucky that any “alerce” giants survived the mass burning of bosque nativo in southern Chile during the 19th century & subsequent logging.

“Abstract
Tree growth rings represent an important natural record of past climate variations and solar activity effects registered on them. We
performed in this study a wavelet analysis of tree ring samples of Pilgerodendron cupressoides species, from Glaciar Pio XI (Lat: 491120S;
741550W; Alt: 25 m), Chile. We obtained an average chronology of about 400 years from these trees. The 11-yr solar cycle was present
during the whole period in tree ring data, being more intense during Maunder minimum (1645–1715). The short-term periods, around
2–7 yr, that were found are more likely associated with ENSO effects. Further, we found significant periods around 52 and 80–100 yr.
These periodicities are coincident with the fourth harmonic (52 yr) of the Suess cycle (208 yr) and Gleissberg (80–100 yr) solar cycles.
Therefore, the present analysis shows evidence of solar activity effect/modulation on climatic conditions that affect tree ring growth.
Although we cannot say with the present analysis if this effect is on local, regional or global climate, these results add evidence to an
important role of solar activity over terrestrial climate over the past 400 yr.
r 2006 Elsevier Ltd. All rights reserved.”

Maybe enough for now. I have to go increase the growth rings of the vegetation in my yard.

“In recent years, researchers have considered the possibility that the sun plays a role in global warming. After all, the sun is the main source of heat for our planet. The NRC report suggests, however, that the influence of solar variability is more regional than global. The Pacific region is only one example.

“Caspar Amman of NCAR noted in the report that “When Earth’s radiative balance is altered, as in the case of a change in solar cycle forcing, not all locations are affected equally. The equatorial central Pacific is generally cooler, the runoff from rivers in Peru is reduced, and drier conditions affect the western USA.”

“Raymond Bradley of UMass, who has studied historical records of solar activity imprinted by radioisotopes in tree rings and ice cores, says that regional rainfall seems to be more affected than temperature. “If there is indeed a solar effect on climate, it is manifested by changes in general circulation rather than in a direct temperature signal.” This fits in with the conclusion of the IPCC and previous NRC reports that solar variability is NOT the cause of global warming over the last 50 years.

“Much has been made of the probable connection between the Maunder Minimum, a 70-year deficit of sunspots in the late 17th-early 18th century, and the coldest part of the Little Ice Age, during which Europe and North America were subjected to bitterly cold winters. The mechanism for that regional cooling could have been a drop in the sun’s EUV output; this is, however, speculative.”

Before taking up your suggestion that I elucidate the challenges of establishing or disproving any relationship between surface temperatures and SSNs, there are two corrections to my last comment that need to be made:

1) The over-estimation of sample VARIANCES of x and y series due to
extraneous components outside the narrow SSN frequency bands is by a factor
of ~4 when the ratio of total spectral power there is ~4, but the effect
upon the normalization of sample cross-correlation is the product of square
roots of ~.8 and ~.2, which is ~.4. Thus the cross-correlation between
coherent signal components in that narrow band may be UNDERESTIMATED by the
reciprocal of that factor, i.e., ~2.5, by the sample values shown here.

2) It’s Figure 2, not Figure 3, that I had in mind when talking about
LAGGING cross-correlation. I suspect that if the computations were
extended to, say, 22yrs, even higher values than those presently shown would
be obtained.

An elucidation to an audience that has scant grasp of spectral methods of
random signal analysis is daunting, but I’ll try to prepare one by Saturday.

Pamela Gray says:
June 12, 2014 at 11:20 am
———————————
Pamela,
The point about UV-A below the thermocline is that although shorter wavelengths of visible light
also penetrate this deep, they do not vary as much as UV between solar cycles.

In terms of detecting the mechanism of solar variation effecting ocean temps over the long term, below the diurnal thermocline is the place to look. This is where the ocean surface thermostat effect has low influence. This is where variation in UV-A can become a cumulative effect. If you just measure SST, you will only see diurnal and seasonal/annual solar variation. (unless you look at a full 150 years)

Konrad, we have measures below SST and below the thermocline. We have physics related to solar insolation at the surface and at depth, meaning below the thermocline. Visible light variations represented as heat will bury UVa variations represented by heat. You have at best a mathematical calculation resulting in a tiny several decimal places fraction of other components far more capable of driving ocean heat trends below the thermocline. Your piddly small UVa driver will never be observed in situ and will never be a left over component after everything else is removed. The figure would be too small for serious scientists to write about.

Milodon, please read your damn citations before you send them. I picked up the first one, and I find the claim that no less than 62 cycles cycles are present at greater than a 95% confidence levels … here they are:

Now, they claim that the sunspot related cycles are 10.9 years, 20.8 years, and 75 years. First off, the claim that the 75-year cycle is solar-related is nonsense, there’s no such cycle in the sunspots. And IF the 10.9-year cycle is the solar cycle, then twice that is 21.8 years, not 20.8 years, so one or the other of these cycles is NOT a solar cycle. Also they claim that 5.0 and 5.8 years are the half cycle of the 10.9 cycle … riiiight.

Finally, what did they look at to get these cycles? Why, tree ring widths.

And how many trees did their sample consist of?

Well … not to put too fine a point on it … one.

One freakin’ tree and you have me wasting my time on this bafflegab?

Milodon, I don’t have time to dig through that kind of crap. Another one you sent was by Briffa and Osborne, which means it’s not believable from the start. And as you point out, there are lots of people making this cockamamie claim.

So let me invite you to take your citations, look through them, and point out to me the ones that you think are real. This one, like every other one of these that I have looked at, dissolved like the rainbow when the clouds are gone … and since you are the one who cited it, it’s your reputation that suffers when it turns out to be garbage. You recommend crap, you’re the one who has to live with the crappy reputation. So let me ask that you use your judgement, that you look before you leap, and my final request, that you stop sending along every garbage claim that you happen to find.

Several highly significant cycles were found, with cycles of c. 15, 30 and 60 years more frequently encountered (Fig. 2). These cycles may be associated with solar activity (e.g. sunspot variability), large-scale circulation of the North Atlantic Ocean and the atmosphere, precipitation cycles, or related to internal hydrological dynamics of the peat bogs. Groundwater fluctuations in peat deposits are the predominant growth-controlling factor for bog-tree growth (Boggie 1972), and as solar and lunar cycles affect groundwater levels, such planetary cycles may be encountered in ring-width chronologies from bog trees. However, the 11- and 22-year sunspot cycles were weak to absent in the records.

They say that the 15, 30, and 60 year cycles “may be associated with solar activity” … how does that work, when there are no such cycles in the sun?

And more to the point, the 11- and 22-year cycles are “weak to absent” … and you send this puerile pabulum on to me as an example of a scientist finding solar cycles in climate records? Really??

I give up, my friend. Your garbage heap is of absolutely no interest, I don’t have time to dig through this kind of nonsense. Your reputation as a judge of climate studies is gone.

While not specifically dealing with the average 11-year cycle, it does provide annual data from which the signal is recoverable. The paper is more oriented toward trying to associate precip with T than with short-term solar activity.

From the study:

A lack of any strong association with monsoon precipitation in our region could also explain why we find only limited association between our QLS precipitation reconstruction and variability in either monsoon intensity inferred from the longer Dongge Cave oxygen isotope data (30) or in total solar irradiance series (reconstructed from cosmogenic radionuclides in polar ice cores and low-latitude tree rings) which have been shown to covary during much of the Holocene (32) (SI Appendix, Fig. SE2).

They specifically say they find little correlation with the solar irradiance … given that, why on earth did you send this to me? The authors themselves say there’s no solar connection, why are you wasting my time with this?

True, but the variation above the thermocline can be subtracted from the record below the thermocline, removing SW variation from cloud and season.

“The figure would be too small for serious scientists to write about.”

We are talking about a 25% variation in over 10 w/m2. Make believe “radiative forcing” from a CO2 doubling from make believe pre industrial levels is only supposed to be around 3 w/m2. I would suggest that both figures should be too small for serious scientists to worry about. But that doesn’t seem to stop anyone.

I wonder why you didn’t pick some of the citations in which the authors themselves found at a high confidence level a signal near this average &/or its 2x.

It’s your reputation as a statistical analyst seriously interested in science rather than special pleading that’s at issue here, not my reputation as a provider of scientific studies.

Which is it? Are you really interested, as you state, in finding out if there is a signal, or in supporting your belief that there isn’t? Ignoring studies that counter your faith opens you to the charge that you are indeed intentionally sailing south from Europe when the New World lies to the west.

I can proffer just as many based upon ice core papers as tree rings. But how about just addressing those studies I linked which found statistically significant signals? You seem heavily invested in no signal. That’s not just an unscientific attitude, but anti-scientific on a Mannian scale. Which I’m sorry to say, since I expected a more thorough analysis from you.

IMO the average 11 year cycle is well supported in precipitation studies since early in the last century. As I noted, this conclusion isn’t even controversial. To vitiate this conclusion, well supported by at least 80 years of observations & even admitted by the Team to some extent, will require more than two dismissive hand waving drive-bys. Well, one drive by & one analysis of a paper whose point wasn’t the 11 year cycle, although I found it in there on that one, too.

I’d be happy to offer five of the many tree ring studies I’ve cited, if you’re willing to undertake them. How about these, in chronological order:

2) Anyone of the following, which include not just tree rings but other vegetative proxies, ie peat bogs:

Rigozo et al. (2002) detected an 11-year cycle in tree-ring width data from Brazil over the period 1837-1996; and Black et al. (1999) reported finding a 12.5- to 13-year signal of climatic variability in the North Atlantic Ocean over the past 825 years. Additionally, Dean et al. (2002) found an approximate 10-year cycle in a lake sediment core obtained from Elk Lake, Minnesota, USA, covering the past 1500 years. Both Rigozo et al. and Dean et al. implicate the sun as the likely source of the approximately 11-year periodicity noted in their records. Black et al. are less enthusiastic about this possibility, but they feel the sun is responsible for driving centennial-scale climate oscillations in their record.

In an analysis of tree-ring chronologies from northeastern Mongolia, Pederson et al. (2001) report “possible evidence for solar influences” on the regional hydrologic cycle. For the period 1651-1995, they reconstructed annual precipitation and streamflow histories for this region, which upon subjection to spectral analysis revealed significant periodicities of 12 and 20-24 years that are believed to be solar-induced.

Nearby in China, Xu et al. (2002) examined plant cellulose ð18O variations in cores retrieved from peat deposits at the northeastern edge of the Qinghai-Tibetan Plateau (32° 46’N, 102° 30’E). Power spectrum analyses of these data revealed multi-decadal periodicities of 79 and 88 years, “suggesting,” in the words of the authors, “that the main driving force of Hongyuan climate change is from solar activities.”

Neff et al. (2001) also provide evidence for a solar-induced influence on the hydrologic cycle. For the period 9,600-6,100 years before present, they investigated the relationship between a 14C tree-ring record and a proxy record of monsoon rainfall intensity inferred from calcite ð18O data obtained from a stalagmite in northern Oman. Their investigation revealed an “extremely strong” correlation between the two data sets; and spectral analyses revealed statistically significant decadal and multi-decadal periodicities of 10.4, 26 and 89 years for the 14C tree-ring record, and 87 years for the ð18O record.

To which I’d add Joe d’Aleo’s solar study, but that’s about T rather than precip.

But if you’re satisfied that you’ve made your case, you’re welcome to that cocoon. IMO my citations aren’t a garbage heap & should be of interest to anyone actually trying to find the signal you claim to be after.

as far as rainfall goes I analysed some good data from a South African station going back to 1927. (-25 degrees latitude)
I report the average annual rainfall in the years:
1927-1950 611.7
1951-1971 587
1972-1995 596.1
1996-2013 641.2
Note the difference in sign of the binomials (100% correlation) for the periods indicated for (minimum) temperature and rainfall?

2) It’s Figure 2, not Figure 3, that I had in mind when talking about
LAGGING cross-correlation. I suspect that if the computations were
extended to, say, 22yrs, even higher values than those presently shown would
be obtained.

Since Figure 2 shows that the correlation at t = 0 is about zero, and the main peak is at ~11-12 years … is your claim, as it appears, that the sst doesn’t respond at all to the current sunspot cycle, but instead is responding to the previous sunspot cycle?

And if so … HTF is that supposed to work??? The sunspot cycle causes no changes today, no changes in five years, but in 11 years it magically appears? Where is the physical mechanism for that?

That’s the part you always seem to ignore. This is not signal analysis in the lab. In the lab, lots of things are possible … but the fact that an entire range of multi-bandpass filters and complex lagging systems can be built in the lab doesn’t mean that they actually exist in the climate system.

And suppose that we look and find (as you speculate) higher correlation with a 22-year lag … are you seriously claiming that would mean that the SST being affected 22 years after the fact by the Hale solar magnetic cycle, but is not affected today by that cycle?

Before answering, please, please, I beg of you, do some test cross-correlations between the sunspot cycle and red noise. You seem to think that if you cross-correlate data X against sunspots and find 11 and 22-year cyclicity, that those results mean something … in fact, what we see in Figure 2 is as common as chips when you run sunspots against red noise, and as a result it is totally meaningless. Run some trials against red noise first, you’re making a fool of yourself with your claims.

Because they found a statistically significant association, even if weak, which is what a reasonable analyst would expect. IMO.

Are you that unclear on citations? I read the study and found no claim of a “statistically significant association” with solar variations anywhere, and I looked hard. All I found is the claim of a “weak association” that I quoted above, I find nothing about a statistically significant relationship.

That claim is useless without a quotation, or a page and paragraph number, or the equivalent. I gave up going on snipe hunts long ago. You want an answer? First you have to provide the data. I’m not going to guess what the heck you might be talking about, that’s a fool’s game.

Catherine, good question. I suggest that you take a look at them and pick the one of the lot that you think is scientifically valid. Let me know which one it is, and I’ll take a look at it.

I’m tired of picking spitballs off the wall. I told this to milodon already, after he piled my desk up to the roof with unsorted garbage, including things that had nothing to do with the sun and said so in the study.

Well, duh … my point was that whatever the ~11-year Schwabe cycle averages out to, the 22-year Hale cycle (which is nothing but sunspots with a polarity reversal on alternate cycles) has to be twice the Schwabe cycle average. But instead, theirs is no where near twice the Schwabe cycle, meaning that either one or the other is spurious.

I wonder why you didn’t pick some of the citations in which the authors themselves found at a high confidence level a signal near this average &/or its 2x.

It’s your reputation as a statistical analyst seriously interested in science rather than special pleading that’s at issue here, not my reputation as a provider of scientific studies.

Which is it? Are you really interested, as you state, in finding out if there is a signal, or in supporting your belief that there isn’t? Ignoring studies that counter your faith opens you to the charge that you are indeed intentionally sailing south from Europe when the New World lies to the west.

I can proffer just as many based upon ice core papers as tree rings. But how about just addressing those studies I linked which found statistically significant signals? You seem heavily invested in no signal. That’s not just an unscientific attitude, but anti-scientific on a Mannian scale. Which I’m sorry to say, since I expected a more thorough analysis from you.

milodon, the papers you have provided to date have been unmitigated garbage. Heck, one of them even said right in the document that they DIDN’T find any solar connection … and you passed it on to me.

I told you then that I was done with you just grabbing the nearest paper and asking me to look at it. You’ve used up your ticket in that regard.

Instead, I suggested that you realize that it’s your reputation on the line, and that trying to pass off horseshit as caviar wasn’t doing your reputation any good. Instead, I asked you to READ AND ANALYZE the documents before sending them on to me.

But in lieu of doing that, you’ve just persisted with more nonsense. Here’s from your very first of the most recent offering, the Douglass paper:

The Arizona trees have a tendency to give a cycle of about 14 years.
The cyclogram of these trees in figure 14 shows the Hellmann relation [a 5.5 year cycle] in
the left quarter, which is the early 125 years of its length. In the central
part, near 1700, it shows an approximate 10-year cycle during the dearth
of sun-spots, and in the right-hand third something close to 21 years.

So this is what passes for your “evidence”? In the first 125 years there’s a 5.5 year cycle, in the next 125 years there’s a 10 year cycle, and in the next 125 years there’s a 21 year cycle … and as a result, the conclusion is that they have “a tendency to give a cycle of about 14 years” … really?

Where is the evidence that this is anything but random fluctuations? Where is the statistical analysis of the results? There is none. Not one measurement of correlation, or any other numerical measure of relationship. No Fourier analysis, no “folding” analysis, no filtering, in fact no analysis at all … is this your idea of a joke?

Instead of actual mathematical analyses, we are treated to graphics … like this one …

That’s not science. That’s worse than graphs I drew in high school. Like I said, amigo, come back when you’ve actually looked through the trash that you are peddling, and found something that you are willing to stand behind. Not just the next piece of nonsense to sucker me into wasting time on, but something you’re willing to put your name behind … oh, wait, you’re anonymous. How foolish of me to mention reputation, it doesn’t matter if your reputation is ruined because you’re peddling trash, you’re anonymous. And of course you can’t put your name behind anything, so there’s no use in asking for that … ah, well, forget it.

In any case, milodon, there’s an infinity of this level of rubbish out there. I’m not interested in reading them all.

So IF you want to pick the very best one that you can find, one that you personally think is actually valid, one that contains actual mathematical analysis of the relationships and not the “gee golly, the two curves look kinda similar, no need for math” bullpuckey that Douglas and another paper you recommended are trying to pass off as science … I’m happy to take a look at that one.

But as far as the random rubbish you’re selling, complete with high-school graphics?

Not interested in the slightest. There are megatonnes of that kind of junk out there, life’s not long enough for that expedition.

You responded, “True, but the variation above the thermocline can be subtracted from the record below the thermocline, removing SW variation from cloud and season.”

Huh???? Go chew on this paper. Something as simple as changes in “chlorophyll a” produced such a variance in shortwave visible light penetration through the mixing layer that the overturning circulation flow slowed. The limit to the paper in terms of your speculation is that they used visible light (300-750nm) as their source which is longer than the narrow UV (10-310nm) you are focused on, yet it is still considered shortwave and able to penetrate far more deeply than infrared. Infrared is considered to be longwave (700nm to 1 mm) and solar infrared is a piece of that (750 nm to 2500nm). That’s why we call solar infrared “shortwave” infrared, because it is in the shorter wave length band of infrared, but it is the shortwave visible light band, not the infrared band, that penetrates deeply into the ocean water column with considerable energy to heat it compared to UVa.

So why did these clearly well-informed scientists endeavor such a task using the visible lightband and ignore UVa, which you seem to think is such a big deal? Here are a few reasons. Below the thermocline layer, there is an infinitesimally small amount of mixing (with the exception of the overturning locations) due to the strong thermocline boundary found between the deep layer and the always expanding and contracting mixing layer. The sub-thermocline is by far the deepest most voluminous layer, strongly replacing warm water which rises through the thermocline barrier hastily into the mixing layer and beyond at the overturning circulation locations in the oceans. Your piddly amount of UVa heating riding that overturning would be undetectable there, if it exists at all, and certainly nowhere else. Seriously.

HenryP, your investigation is extremely poor from what I have read so far. Do you have this written up with introduction, literature review, problem statement (which usually is stated in terms of either new information needed for a new or potentially new observation, or an accepted hypothesis is not sufficient to explain a current observation), your hypothesis against the null hypothesis, methods, results, discussion, and conclusion? You said you have investigated but I can’t find it anywhere.

Speaking of the global ocean’s deep layer, thermocline and mixing layer, it would be interesting to speculate that the mixing layer is the thing expanding up, not the deep layer. If that is the case, the rising sea level would all be due to a warming of the mixing layer since at least the LIA. If the thermocline level, measured from the perpendicular ocean bottom to the thermocline, averaged out to be at the same level, but the mixing layer on top of it is rising, the case would be nearly open and shut that sea level is not rising due to more water being added to the oceans, but to heat expansion in the mixing layer. If that is the case, projected sea level rise, which is currently considered to be anthropogenically related to added water due to melting land ice and ground water extraction, would necessarily have to change to a metric based on LIA-related, if not the last glacial period, heat rise expanding the mixing layer and little else.

Of the issue related to melting land ice, a good deal of that melting ice goes into the ground, potentially offsetting any increased ground water extraction. So that part of the equation can possibly be ignored under the anthropogenic hypothesis.

How to stop complete analytic foolishness that ensues from simplistic
presumptions?

A good starting point should be the recognition that the m-lagged sample
cross-correlation function

ccf(m) = cov(x,y;m)/(sigx*sigy)

does NOT have the same straightforward meaning that it does in ordinary
linear regression of time-invariant data. The power spectra of the
data-series comes into play via their effect upon the normalization
sigx*sigy, which is independent of inter-relationship, while the covariance
is not! In fact, it expresses in the lag-domain the average time-varying
characteristics ONLY of variously related signal components, which may be
in entirely different frequency ranges. Proper interpretation
of the sample ccf thus requires complete SPECTRAL knowledge of both data
series.

Among the many misconceptions that novices make in viewing sample ccfs is
that low correlation at zero-lag indicates little relationship between
series. That is true ONLY IF there are no time-lags involved. But
physical systems often display phase-lag between excitation and response!
With 90 degrees systematic lag, the ccf at zero lag is zero for purely
sinusoidal forcing. The analytic fact that the ccf maximum then occurrs at
a quarter-cycle lag in that simple case leads to another egregious
misconception: that the ccf maximum indicates some physically meaningful lag
relationship in the general case with continuous power densities. Only if
one data series is shifted wholesale by a constant to produce the second
series can such a conclusion hold.

To obviate such pitfalls, competent analysis of signal relationships relies
upon cross-spectral methods . Defined by the Wiener-Kintchine theorem
as the F. transform of the covariance function, the cross-spectrum is
complex-valued. Its real part is the co-spectrum C(f), obtained by the
cosine transform; its imaginary part is the negative of the
quadrature-spectrum Q(f), obtained by the sine transform. The crucial
information about signal relationships in the general case that
cross-spectrum analysis provides for each frequency-band is the squared
coherence:

R^2 = (C^2 + Q^2)/(Sx*Sy)

where the S denote the power spectra of the individual data series. The
confidence intervals for this vital metric–a spectral analog of simple
R^2–have been tabulated by Amos & Koopmans for various degrees of freedom
of spectral estimation. The relative phase between coherent signal
components in the same frequency band is given by:

phi = arctan (-Q/C)

Obtaining valid sample estimates of these quantities is nowhere near as
simple as merely utilizing the complex FFT coefficients of the ccf out to
lags of half the data length. It does require, however, the ccf values far
beyond what has been shown here in Figures 2 and 3. Cross-correlating SSN data with red noise simulations might obtain spurious max values comparable to what is seen there, but it could never obtain the 99% confidence in coherence levels shown by competent cross-spectrum analysis.

The highly significant coherence in a narrow band between SSN and SST can scarcely be revealed by the simplistic methods used in this post. If they were used in other contexts where detection of weak signals is vital, we would have no cell-phone communications, nor Dolby noise reduction, and enemy submarines with ballistic missiles (truly bad SSNs) would lurk totally undetected in our coastal waters.

Why are you so studiously ignoring the many citations that mention the ~11 year cycle right in the title of the papers?

Why are you so studiously ignoring what I said to you when you asked that question last time? To remind you, it was:

Catherine, good question. I suggest that you take a look at them and pick the one of the lot that you think is scientifically valid. Let me know which one it is, and I’ll take a look at it.

I’m tired of picking spitballs off the wall. I told this to milodon already, after he piled my desk up to the roof with unsorted garbage, including things that had nothing to do with the sun and said so in the study.

Pick any one you want. Your request for specific selections from among the many offered was already answered. Pick any of these. But to support your unfounded assertion that there is no evidence of an 11 year cycle, you’ll need to examine all of the studies that have found it.

Why do you expect everyone else to do your literature searches for you? Instead of attacking us for “piling up garbage”, why don’t you do what real science paper writers do & conduct an exhaustive literature search before making baseless assertions?

This is what got you in trouble with Roy Spencer and causes you to claim to have discovered phenomena already well known to real scientists. Your published “paper” violated one of the key rules for scientific article writing so well summarized on this blog by Pamela Gray, ie a review of literature. You must know that that is standard practice, since in your “paper” you state that you’re not following the usual rules.

Are you too lazy to conduct thorough searches, or afraid of what you’ll find? Or are you cutting corners in hopes of trying now to have a scientific career late in life? Real scientists have classes to teach and other demands on their time, so you can’t plead that your work keeps you from doing an adequate review before making baseless assertions. If it’s your illness, I’m sorry, but you should not make bold claims without any basis, which by the nature of your assertion requires that you look at every study you can find before so asserting.

The citations reproduced here are just a few of the many papers you need to read and analyze before you can even begin to claim there is so 11 year signal in climatological observations. It’s against the scientific method to assert a claim, then expect everyone else to do your research for you.

Here are some of the studies previously cited here & so studiously ignored by you:

1) Rigozo et al. (2002) detected an 11-year cycle in tree-ring width data from Brazil over the period 1837-1996; and Black et al. (1999) reported finding a 12.5- to 13-year signal of climatic variability in the North Atlantic Ocean over the past 825 years. Additionally, Dean et al. (2002) found an approximate 10-year cycle in a lake sediment core obtained from Elk Lake, Minnesota, USA, covering the past 1500 years. Both Rigozo et al. and Dean et al. implicate the sun as the likely source of the approximately 11-year periodicity noted in their records. Black et al. are less enthusiastic about this possibility, but they feel the sun is responsible for driving centennial-scale climate oscillations in their record.

2) In an analysis of tree-ring chronologies from northeastern Mongolia, Pederson et al. (2001) report “possible evidence for solar influences” on the regional hydrologic cycle. For the period 1651-1995, they reconstructed annual precipitation and streamflow histories for this region, which upon subjection to spectral analysis revealed significant periodicities of 12 and 20-24 years that are believed to be solar-induced.

3) Neff et al. (2001) also provide evidence for a solar-induced influence on the hydrologic cycle. For the period 9,600-6,100 years before present, they investigated the relationship between a 14C tree-ring record and a proxy record of monsoon rainfall intensity inferred from calcite ð18O data obtained from a stalagmite in northern Oman. Their investigation revealed an “extremely strong” correlation between the two data sets; and spectral analyses revealed statistically significant decadal and multi-decadal periodicities of 10.4, 26 and 89 years for the 14C tree-ring record, and 87 years for the ð18O record.

Abstract
Spectral and wavelet analysis were performed on a tree ring width time series obtained
from a 2500 yr old cypress tree (Fitzroya cupressoides) from Costa del Osorno, Chile.
The periods for analysis were selected at 95% confidence level. Both periodicities
characteristic 5 of solar activity and climatic variations were found in this tree ring width
series. The 11 and 22 years solar cycle periods were present in tree ring data with a
confidence level above 98%. This indicates the solar modulation of climatic variations
is being recorded by the tree ring grown. However wavelet analysis shows that these
are present only sparsely. Short-term variations, between 2–5 years, are also present
10 in tree ring data, and are shown by wavelet maps to be a more permanent characteristic.
This time scale is a signature of ENSO events. Long-term variations, above 200
years, are also present in tree ring data. The spectral analysis performed in this work
shows that this species has the ability to record solar-ENSO variations that seems to be
affecting the local environment of tree growth, and also that this region was influenced
15 by ENSO events at least in the past 2500 yr interval covered by this study.

Abstract
Tree growth rings represent an important natural record of past climate variations and solar activity effects registered on them. We
performed in this study a wavelet analysis of tree ring samples of Pilgerodendron cupressoides species, from Glaciar Pio XI (Lat: 491120S;
741550W; Alt: 25 m), Chile. We obtained an average chronology of about 400 years from these trees. The 11-yr solar cycle was present
during the whole period in tree ring data, being more intense during Maunder minimum (1645–1715). The short-term periods, around
2–7 yr, that were found are more likely associated with ENSO effects. Further, we found significant periods around 52 and 80–100 yr.
These periodicities are coincident with the fourth harmonic (52 yr) of the Suess cycle (208 yr) and Gleissberg (80–100 yr) solar cycles.
Therefore, the present analysis shows evidence of solar activity effect/modulation on climatic conditions that affect tree ring growth.
Although we cannot say with the present analysis if this effect is on local, regional or global climate, these results add evidence to an
important role of solar activity over terrestrial climate over the past 400 yr.

How to stop complete analytic foolishness that ensues from simplistic
presumptions?

A good starting point should be the recognition that the m-lagged sample
cross-correlation function

Actually, a good starting point to ending “analytic foolishness” would be your worked example using Nir Shaviv’s data. Nir Shaviv has provided his worked example. I’ve provided mine, using the same data.

And once again, you’re all mouth, nothing but theory and no results. You might be 100% correct, 1sky1, you seem like a smart guy. But claiming you are correct hardly demonstrates it. So far, you’ve talked the talk, but you’ve never walked the walk.

Here we have a clear example. Shaviv claims that there is a significant correlation between sunspots and SSTs. I say no. I’ve posted up the sunspot and the SST data.

So what does your whiz-bang method say, 1sky1? Are they significantly correlated or not? Mumbling about theory doesn’t cut it, my friend. It’s time for a worked example.

Or not, your choice. I’ve heard plenty of hilarious reasons from you as to why you never produce any actual results, but I doubt that you’ve run out of excuses yet …

Pamela Gray says:
June 13, 2014 at 11:05 am
——————————–
Thank you for the link to that paper, there was much of interest. While it was a modelling paper, the proposed effect of ocean chlorophyll changes seems plausible.

You ask – “So why did these clearly well-informed scientists endeavor such a task using the visible lightband and ignore UVa, which you seem to think is such a big deal?”

The answer is simple, they were looking at simply the changes in ocean circulation and heat content due to changes in the depth to which radiation penetrates in the oceans. Funny thing. This is exactly the type of mechanism I am talking about.

You claim these are “clearly well-informed scientists”. I concur. They are very, well informed.
While they do not use the correct engineering terms “selective surface” or “selective coating”, they do know that the oceans are not responding to incident radiation as a “near blackbody”, and the depth at which radiation is thermalized in the oceans is very important. Note this statement from the paper starting –

“As a first step, most models assume that all of the solar irradiance is absorbed at the surface in the same way that latent and sensible heat are passed across the air-sea interface. In an effort to provide a more realistic…”
They are correct in that the oceans are not a “near blackbody” they are a selective surface. So what is a selective surface? Willis always says “do the maths”. I always say “do the empirical experiment”. Maths is not physics. Maths can be used model physics, but it can also be used to model non-physical mechanisms, a chronic problem in climastrology.
Here is a simple empirical experiment entitled “Shredded Lukewarm Turkey in Boltzmannic Vinegar” –

The experiment is simple, both acrylic blocks have equal IR emissivity and equal ability to absorb UV & SW. The only difference between the blocks is the depth of UV/SW absorption. For dramatic results expose the blocks to full sun for 3 hours. The average temperature of block A will be around 20C higher than block B. The base temperature of block A will be around 40C higher than block B.
If exposed to intermittent SW even the surface temp of block A will exceed that of block B.
The depth of SW/UV absorption in a transparent material with a slow rate of internal non-radiative transport and an IR emissivity <1 is critical to determining the resultant temperature of such a “selective surface” exposed to intermittent UV/SW radiation.
Because of convective circulation, turbidity and surface roughness, the oceans are far more complex than this. However, below the diurnal over turning layer, the oceans will act in a manner analogous to the simple selective surface experiment. Here the effects from variation in UV/SW radiation can be cumulative.
At 10 w/m2 at 50m depth it is plausible that UV variation between solar cycles could cause a tiny accumulation of 0.8C in 150 years.

Pamela Gray says:
June 13, 2014 at 11:43 am
———————————
It has been estimated that while the rate may have decreased recently, sea levels have been rising at around 300mm per century. In 150 years since the LIA, this would be around 450mm. If this were from melting land ice it would represent a layer over 1m deep over the total land surface of the planet. This does not seem plausible.
However ocean warming due to incident LWIR is not possible for water that is free to evaporatively cool. Therefore variation in atmospheric radiative gas concentration is also not a plausible mechanism for sea level rise.

Mechanisms that may be plausible are –
– UV/SW variation between solar cycles accumulating in the oceans.
– Variation in ocean turbidity (biological or mineral)
– Variation in cloud cover.
– Volcanic activity.

Catherine Ronconi said June 14, 2014 at 2:58 pm, replying to Willis, in part:
“…Why are you so studiously ignoring the many citations that mention the ~11 year cycle right in the title of the papers?…”

First, I quite honestly don’t know where the “many citations that mention the ~11 year cycle right in the title of the papers” are. Please direct me – I MIGHT well have missed the links. All I found above that had a mention of an 11-year cycle is Hood et al. The abstract suggests a finding in some part of the N. Pacific, but not in the Arctic! I can’t see the full paper which is paywalled. (I miss the days in academia when the papers just loaded and we never thought about restricted access.) In any event, exactly where are the “many citations” particularly as they might relate to sea-surface temperature and not rainfall over land (like the Tibetan Plateau – a well-known sea), or something like that.

But if there are really “many citations”, than a reader here might well expect a dozen or so could be easily listed, or a link compiling listings to many papers could be posted. Where are they? Please give Willis and the rest of us some consideration and list a few items, or better, “many” of them.

I have no doubt there is a 11-year (very approximately) solar cycle for SOME things. Two at least. One of these is the SSN on the sun itself, obvious enough. As for the earth itself, I am personally familiar with only one thing – traffic on the 10 meter ham-radio band, which peaks with the sunspots. I doubt 10-meter ham-gabbing is related to sea surface temperatures. I thought it had to do with ions at the top of the atmosphere. It does correlate well with the Maunder Minimum though – there was no 10-meter ham traffic for that entire 70 years!

I have no doubt there is a 11-year (very approximately) solar cycle for SOME things. Two at least. One of these is the SSN on the sun itself, obvious enough. As for the earth itself, I am personally familiar with only one thing – traffic on the 10 meter ham-radio band, which peaks with the sunspots. I doubt 10-meter ham-gabbing is related to sea surface temperatures. I thought it had to do with ions at the top of the atmosphere. It does correlate well with the Maunder Minimum though – there was no 10-meter ham traffic for that entire 70 years!

As a ham myself (H44WE), that comment had me spilling my morning coffee on the keyboard. My thanks, and a happy Fathers Day morning to you all.

Catherine Ronconi said June 15, 2014 at 1:17 pm:
“….I don’t know how you MIGHT have missed the many references to 11 year cycles cited above. I found them easily. See prior comment for A FEW of them. Or use the FIND function & search for “11″. …..”

Search for ’11”. That’s very funny! I don’t suppose you tried to use a FIND on “11” before suggesting it to me. I tried that last night on this post and there were well over 400 instances then, 484 when I tried it just now. Not much help. It was a joke – right?

Onward. Let’s try your first suggestion: Rigozo et al. (2002)” which is not much of a lead! A bit of searching goes through a CO2-Science site and onward to Advances in Space Research and finally the paper title “Solar variability effects studied by tree-ring data wavelet analysis” complete with an invitation to lighten my wallet by $35.95 if I would like to actually see it. (The title apparently is not one of your “many” with 11 years right in the title. It does have the redeeming virtue of NOT adding another “11” to the FIND total!) Nor is there an 11 in the free abstract.

Willis Eschenbach says:
June 14, 2014 at 10:00 pm
…
Pick any one you want. Your request for specific selections from among the many offered was already answered. Pick any of these. But to support your unfounded assertion that there is no evidence of an 11 year cycle, you’ll need to examine all of the studies that have found it.

Why do you expect everyone else to do your literature searches for you? Instead of attacking us for “piling up garbage”, why don’t you do what real science paper writers do & conduct an exhaustive literature search before making baseless assertions?

Since I haven’t been able to find a single example that stood up to examination, and I won’t live long enough to investigate them all, I asked people to point to the study that they thought would hold up. That way, I figured that the people holding the view would tell me about the study that really was good, that was solidly constructed and analyzed, that would stand up to examination.

Instead, you and others took it as an invitation to pile every crappy paper you could find on me, even some papers that (like the Black paper you cite below) specifically state that they are NOT about the 11-year sunspot cycle. Not what I expected.

I expected that the champions of the idea that the sun affects the climate would seize the chance to show me, and the world, that there is a study that actually will stand up to close examination … foolish me. Foolish me.

So I’ve been trying to get across to you and others that yes, I know that there are literally dozens and dozens of studies out their making the claim that they’ve found the elusive 11-year cycle. So what? After the climate “consensus” debacle, are you seriously claiming that a bunch of papers that all agree with each other are therefore right?

What I’d like is to cut through the fog, and ask the adherents of the “It’s The Sun, Stupid” point of view tell me what they see as the rock solid evidence, the real study.

Instead, you say it’s my job and try to bury me under random garbage, and insult me in the process. Well, OK … but if you guys that believe in the theory won’t tell me where the good solid study is to be found, I’ll continue to say that I haven’t found it …

(Please note that I have been clear from the first. I have emphasized that I was NOT saying that the sun has no effect on the climate. What I WAS saying was that to date, I’ve not found a single climate dataset that shows a statistically significant ~11-year solar signal.)

This is what got you in trouble with Roy Spencer and causes you to claim to have discovered phenomena already well known to real scientists. Your published “paper” violated one of the key rules for scientific article writing so well summarized on this blog by Pamela Gray, ie a review of literature. You must know that that is standard practice, since in your “paper” you state that you’re not following the usual rules.

Dear heavens, are you really that dense? CITE OR QUOTE YOUR ASSERTIONS! Which “paper” are you babbling about without identifying? I’ve written about 500 posts and now you think I can read your mind?

Also, what got Roy Spencer in trouble is that he didn’t bother to read what I’d written before making his accusations. Fortunately, the internet never forgets, so I was able to clearly show that he (and now you as well) were just slinging mud.

Do your homework, Catherine, your dislike of me is affecting your judgement. Roy was simply wrong in his claim that I had not cited Ramanathan, and he was simply wrong that Ramanathan’s hypothesis was the same as mine. I had cited Ramanathan, and the two hypotheses are totally dissimilar. Lucky for me, it’s all there in black and white.

Are you too lazy to conduct thorough searches, or afraid of what you’ll find? Or are you cutting corners in hopes of trying now to have a scientific career late in life? Real scientists have classes to teach and other demands on their time, so you can’t plead that your work keeps you from doing an adequate review before making baseless assertions. If it’s your illness, I’m sorry, but you should not make bold claims without any basis, which by the nature of your assertion requires that you look at every study you can find before so asserting.

My dear, I have looked at every serious study of the sunspot cycle that I’ve found. I’ve skipped what I see as the garbage. In the process, as near as I can tell, I have not only read but analyzed and falsified more important papers on the subject than anyone that I know of. I have shown that there is no 80-year Gleissberg cycle. I’ve shown that the Christensen-Lassen papers don’t hold water. I’ve shown that Nir Shaviv’s SST results are not statistically significant. I’ve shown that the ever-cited Parana River study was a joke. I’ve even dug out and looked at Herschel’s original claims about wheat prices and sunspots. I’ve analyzed a variety of climate datasets on my own, and dug deeply into the claims of others. Not only that, I’ve taken the time to write all of those studies up for jerks like you to jeer at and for serious folks to read and discuss and add to, plus I have posted the hundreds and hundreds of lines of computer code that lazy old me wrote and debugged plus the data to allow replication, plus I’ve answered inane comments such as yours by the hundreds and hundreds … and I also have a day job building houses.

Lazy? All that accusation does, Catherine, is prove to the lurkers that your judgement sucks begonias, and that you are willing to throw baseless mud.

The citations reproduced here are just a few of the many papers you need to read and analyze before you can even begin to claim there is so 11 year signal in climatological observations. It’s against the scientific method to assert a claim, then expect everyone else to do your research for you.

I have not asked anyone, including you, to “do my research for me”. I’ve asked you to point out what you think are good studies SO THAT I COULD DO MY RESEARCH FOR ME, because if I wait for you or anyone out there to “do my research for me” I’ll wait forever. You do my research for me? Don’t make me laugh. Let us know when you analyze your first paper or dataset on the subject, I’m sure Anthony would consider publishing it if it’s good.

The problem is that there is so much crap out there on the subject that I couldn’t possibly analyze it all if I could live a hundred years. So I asked for assistance from adherents of the idea to identify the good stuff. Now, if you want to assist in that regard, fine, and if you don’t, piss off … but don’t try to claim I’m wrong for asking for assistance in shoveling out the Augean Stables. If the adherents of an idea can’t point to a valid study supporting the idea, that means something.

You go on to say:

Here are some of the studies previously cited here & so studiously ignored by you:

1) Rigozo et al. (2002) detected an 11-year cycle in tree-ring width data from Brazil over the period 1837-1996; and Black et al. (1999) reported finding a 12.5- to 13-year signal of climatic variability in the North Atlantic Ocean over the past 825 years. Additionally, Dean et al. (2002) found an approximate 10-year cycle in a lake sediment core obtained from Elk Lake, Minnesota, USA, covering the past 1500 years. Both Rigozo et al. and Dean et al. implicate the sun as the likely source of the approximately 11-year periodicity noted in their records. Black et al. are less enthusiastic about this possibility, but they feel the sun is responsible for driving centennial-scale climate oscillations in their record.

This is exactly why I don’t look at every study. Black says the cycles are 12.5 to 13 years. Rigoso says 11 years. Dean says 10 years. Obviously, they can’t all be right … and Black even says it’s doubtful that his results are related to the sunspot cycle. Am I “studiously ignoring” Black? No, I’m paying attention to it, and as a result I know I don’t need to read it.

You see why I asked you not to just shovel this kind of garbage, but to look through them and pick the one you think will stand up?

Seriously, Catherine, look at it this way. I have offered, of my own generosity, to allow you to select one study for me to analyze. If you don’t want to take me up on that offer, fine.

But if you do, the offer still stands. Where is the study that you think proves the case? And if you don’t have such a study … why are you on my case?

In the meantime, I won’t hold my breath. While you are sitting around criticizing, lazy old me will just continue to analyze the studies that seem like they have a chance of not falling apart as soon as they are looked at in detail.

I wouldn’t have insulted you if you didn’t make it a practice to insult others so regularly, suffering as you do from delusions of competence.

The fact is that you have been too unstable to have had a normal career, so now are trying to feed your megalomania by ripping off the ideas of people who have devoted their lives to science. You haven’t changed since your mental problems surfaced in the Army.

Why do you want me to pick one study? How about you pick one of the many showed you that overtly in their title or abstract find a quasi-decadal signal?

If you insist on my picking one, then how about one of the two Chilean studies to the abstracts of which your attention has repeatedly been directed. Better yet all of them.

If you don’t have the time to review all the relevant literature, then how dare you make the easily demonstrated false claim that there is no signal? By its very nature, such a claim should only come after exhaustive analysis of at least dozens if not hundreds of papers. Instead, like so many other losers working out their anger on message boards and blogs, you aggressively state a position then tell the sane people to find exceptions to it for them. I’ve seen this pathological behavior over and over, which is one reason why I rarely comment or post, but find it more instructive to observe.

Catherine Ronconi said June 15, 2014 at 4:54 pm:
“I did and found lots of abstracts and titles quoted with references to a 10 to 12 year cycle, mostly 11.”

Sorry. You say “I did” but not what you did. I assume it must have been the FIND on “11” in this post that I was worried about? Is that right? Well I just went through all (now 492 occurrences) and found NO “abstracts and titles quoted with references to a 10 to 12 year cycle, mostly 11.” We MUST be doing something entirely different. I certainly could be missing something. Please help.

Hopefully it is not too much to ask that you do, as seems to be the tradition on WUWT and as a matter of common courtesy, a list the specific clickable links to perhaps two or three of these papers (at least one please). Others do this much and much much more. Please choose papers that can be downloaded in full without charge. Or if these can’t be found, please write up a brief summary of your own reading of the papers you cite, addressing the concerns that have been voiced on this posting.

How can you possibly have missed them when I recopied two of the abstracts previously linked here with “11 year cycles” in them in a comment to which you replied? But here for at least the third time are those two:

Abstract
Spectral and wavelet analysis were performed on a tree ring width time series obtained
from a 2500 yr old cypress tree (Fitzroya cupressoides) from Costa del Osorno, Chile.
The periods for analysis were selected at 95% confidence level. Both periodicities
characteristic 5 of solar activity and climatic variations were found in this tree ring width
series. The 11 and 22 years solar cycle periods were present in tree ring data with a
confidence level above 98%. This indicates the solar modulation of climatic variations
is being recorded by the tree ring grown. However wavelet analysis shows that these
are present only sparsely. Short-term variations, between 2–5 years, are also present
10 in tree ring data, and are shown by wavelet maps to be a more permanent characteristic.
This time scale is a signature of ENSO events. Long-term variations, above 200
years, are also present in tree ring data. The spectral analysis performed in this work
shows that this species has the ability to record solar-ENSO variations that seems to be
affecting the local environment of tree growth, and also that this region was influenced
15 by ENSO events at least in the past 2500 yr interval covered by this study.

Abstract
Tree growth rings represent an important natural record of past climate variations and solar activity effects registered on them. We
performed in this study a wavelet analysis of tree ring samples of Pilgerodendron cupressoides species, from Glaciar Pio XI (Lat: 491120S;
741550W; Alt: 25 m), Chile. We obtained an average chronology of about 400 years from these trees. The 11-yr solar cycle was present
during the whole period in tree ring data, being more intense during Maunder minimum (1645–1715). The short-term periods, around
2–7 yr, that were found are more likely associated with ENSO effects. Further, we found significant periods around 52 and 80–100 yr.
These periodicities are coincident with the fourth harmonic (52 yr) of the Suess cycle (208 yr) and Gleissberg (80–100 yr) solar cycles.
Therefore, the present analysis shows evidence of solar activity effect/modulation on climatic conditions that affect tree ring growth.
Although we cannot say with the present analysis if this effect is on local, regional or global climate, these results add evidence to an
important role of solar activity over terrestrial climate over the past 400 yr.

“I expected that the champions of the idea that the sun affects the climate would seize the chance to show me, and the world, that there is a study that actually will stand up to close examination … foolish me. Foolish me.”

Wait, I thought the thread was just about some elusive 11 year signal, and there was no claim that solar influence of climate could be dismissed if such a signal could not be found? Foolish me? No, I can see I’m not the only one who can see what this is really all about.

(1) We saw these much earlier and they were not yours – from Milodonharloni. Both are the same author. The first was based on “a tree”! The second says “trees” and that the data is from Rigozo et al (2006a), apparently in Trend Appl Sci Res, so we don’t know how many here – I guess two at least! Fig. 1 of this 2007 paper plots “Tree ring width” so I guess we are back to one tree. BS.

(2) I can not reply and ignore something else too. For the record: With regard to your attack on Willis, you have redefined uncouth beyond the pale.

I never claimed that they were originally mine. Where did you get that idea? I referenced them. What is your major malfunction?

What in my comments about Willis was wrong? Have you not read his vicious ad hominem attacks on others & dismissal of others’ work as garbage? Maybe you missed the graphs he posted without citing sources, misleading readers that they were original with him, until their sources were cited by other commenters here?

But if you like so many here want to remain a fan boy of such a character, you’re welcome to him.

I was nuts ever to comment again on this blog populated by crackpots who give catastrophic man-made climate skepticism a bad name after leaving the last time.

M Simon says:
June 15, 2014 at 8:44 pm
———————————-
It is indeed germane to this post as they are looking at why an 11 year solar signal will not be identifiable in temperature records.

If you read some of my responses to Pamela on this thread, you will note that I am describing a plausible physical mechanism (UV variation below the ocean thermocline) for solar influence on climate that would create long term signal but mask shorter single solar cycle signals.

Thanks! I’ll have a look. The thread is so long I passed up reading it in favor of just posting that comment. I had read the original post when it came out. Which is how I knew where to leave the link.

I wouldn’t have insulted you if you didn’t make it a practice to insult others so regularly, suffering as you do from delusions of competence.

The fact is that you have been too unstable to have had a normal career, so now are trying to feed your megalomania by ripping off the ideas of people who have devoted their lives to science. You haven’t changed since your mental problems surfaced in the Army. …

Dang … she drove that bus right straight over the edge and down the precipice … that’s an impressive stunt. I did like some of the more subtle touches, too, like how it’s my fault that she has stooped to insults.

Why do you want me to pick one study?

Been there … explained that …

If you insist on my picking one, then how about one of the two Chilean studies to the abstracts of which your attention has repeatedly been directed. Better yet all of them.

Catherine, you’re not paying attention. I already looked at one of the Chilean tree-ring studies and reported my findings way upthread. Their results showed that the solar variations they referred to are scarcely visible down in the weeds. And their huge dataset consists of one single tree … However, there is a larger problem—their data isn’t archived, so no analysis is possible. And because it can neither be analyzed nor falsified, this of course means that their study is not science in any sense of the word.

Just for completeness, I looked at the other analysis just now. Their data isn’t archived either. What is it with these anecdotal studies? If the data isn’t archived, the study can’t be falsified, and as a result it’s not science of any kind. Far too many of these studies reference such unobtainable or “gray” datasets.

And their conclusions simply don’t agree with their graphics. Here’s their wavelet analysis of the tree rings:

ORIGINAL CAPTION: Fig. 1. … The wavelet spectrum with cone of influence (smooth curve) and significance levels contour for 95%. At right the legend indicates the wavelet spectral power in colors levels (b).

Now, take a look at that analysis. There is a short period of time around 1650-1700 where there is a bit of power in the 11-year band. For the other 90% of the time there is NO POWER IN THE 11-YEAR RANGE.

So how do they describe this situation? Read’m and weep … emphasis mine:

Fig. 1 shows the wavelet spectrum for the Chile tree ring data. It shows a signal associated to the 11-yr solar cycle and fourth harmonic (52 yr) of the Suess cycle (208 yr), in the interval 1645–1715, and a strong signal associated with the Gleissberg (~80–100 yr) solar cycles between 1720 and 1860.

That’s a load of bollocks, or at least it’s stretching the truth like boardwalk taffy … a “signal associated to the 11-year solar cycle”? Yeah, weakly for a few years, and it disappears entirely after that. That’s your evidence? Really?

So there you have it, Catherine. Both of the studies you reference are totally un-falsifiable, because they have not archived their data. And neither of them shows any significant strength in the ~11-year range.

In any case … suppose that the researchers in the first paper are correct and they actually did, after a couple of centuries of searching, find one single solitary tree that shows a solar signature … do you find that significant? Two centuries searching, and the searchers are impressed enough by one pathetic tree enough to publish it? Are you that impressed?

And if you do find it significant … do you understand what “significant” means?

Finally, I’ve never said that the sun has no effect on the climate.

What I am saying is that people have been searching for the ~11-year signal for a couple of centuries, and if your best shot at proof is a single freakin’ tree in Chile … well, to me that means that if the signal is there, it’s damn near invisible, or people wouldn’t be publishing results about one pathetic tree. And the single tree agrees—it says the solar-lengh cycles are tiny, way down in the noise, with LOTS of larger cycles.

To summarize, Catherine, I’ve given you the opportunity to tell me what you see as the best evidence for the solar-climate connection. I’ve looked at your best evidence, and it’s a joke—the data isn’t even archived, and the claimed results are not borne out by the graphics and other information. Nice try, though, and thanks for playing.

So … does anyone else have evidence that they think supports the existence of an 11-year cycle in the climate? Where is the dataset that actually contains such a signal, and how can we reveal it?

“So … does anyone else have evidence that they think supports the existence of an 11-year cycle in the climate? Where is the dataset that actually contains such a signal, and how can we reveal it?”

Nope, no amount of 11 year cycle threads will do it. Not looking for an 11 year cycle. Just 0.8C in 150 years. (good thing too, looks like David Evans is about to blow the whole “I can’t find an 11 year cycle so solar variation doesn’t effect climate” game out of the water).

“The world wonders …”

Well, some may be wondering when that “DWLWIR slows the cooling of the oceans” hole is going to be deep enough. The JCB here at WUWT has been working tirelessly since 2011 ;-)

Q. Why did the climasstrologists just try to dismiss SST as a metric of global warming?

WUWT is at the bottom of a hole you dug.

A. !ohw sseuG

And who was it supposed to be? Who did I want it to be?

No matter, now it looks like it’s going to be David.

[The mods note that it is the bottom of a hole that does the work … You dig the hole to make the bottom deep enough! .mod]

Konrad, here’s a free clue. That bit of bizarre babbling and backwards writing makes no sense at all from this side of the screen. For example, you say “Who did I want it to be” … who did you want what to be?

But you found the evidence of the “notch filter” yourself. The absence of the 11 year signal. And you know that the Maunder minimum shows up in the temperature record. So “low” frequency signals (in the case of Maunder several decades of low SSNs) get through. The question then is what causes the 11 year “notch” ?

Jo and David claim to have found a mechanism to explain it. We shall see in due time if they have.

In fact it was your analysis here that got me excited about what David/Jo have claimed to have found.

M. Simon, remember to rule out the first encountered pathology before digging passed the wax in the ear canal looking for a reason for the hearing loss. The Maunder Minimum does not track well at all with SSN. It tracks closely with known pulses of sulfate (and even pieces of ash) in ice cores. Between the two, an aerosol-caused diminution of solar surface insolation is the most likely cause of such a radical change in temperature versus the tiny change at the TOA. Two reasons, there is ample evidence of this aerosol load that tracks well with temperature proxies, and that kind of aerosol load is quite adequate to the task of directly (and with additional aerosol pulses, continuously) decreasing solar insolation, no amplification required. The evidence of changes at the Sun’s surface affecting on the ground temperatures is not evident at all without a tremendous amount of data massaging and using skating-on-thin-ice selective subjects.

Except that the Maunder Minimum was named after the dearth of sunspots observed during the decades c. 1645 to 1715. Spörer noted this lack of sunspots in the observational record during his solar studies in the 1880s. Here is Eddy’s classic 1976 paper on the Maunder Minimum:

“Scripps Institute has carbon dioxide monitoring sites around the Pacific from Alert Station, Canada, to
the South Pole. The data have been collected daily from some sites since 1958. Each of their sites was selected
to have a minimum influence from man made sources of carbon dioxide. They are intended to be background
sites. Concentrations in cities and near industrial sources are often several times background levels. Plots of the
raw daily flask data reveal some spikes in concentration indicating influence of local sources that are not
representative of background. These data are flagged and not included in daily or monthly averages.”…

“The vapor pressure of carbon dioxide is a function of the thermodynamics of sea water
containing carbonate ions, dissolved carbonates, their solids, as well as dissolved carbon
dioxide. Decaying organic matter is another source of carbon dioxide in sea water. There is
a lot more of it in the oceans than there is on land. The sea becomes a source when SST
rises and a sink when it falls. The rate of emission or absorption depends on the rate and
direction of temperature change. That rate is constantly changing with space and time. A
good example of this is illustrated by the four SST data sets of the Nino regions across the
tropical Pacific. The temperature rises as the water goes from East to West across the
Pacific. The rise is linear and the rate of rise varies seasonally and is not constant from year
to year. The seasonal variation is associated with the northern Pacific circulation. Besides
the seasonal variation there are two other statistically significant cycles. One at 11 years is
stronger than one at 176 years. Nearly all the rates are positive and vary by an order of
magnitude within six months. Thus, the tropics are nearly always a source of carbon
dioxide and the strength of that source changes by an order of magnitude within a relatively
short time.”…

“The warmest part of the Pacific is around the western equator. I calculated SST for 160
East using the Nino rate of warming data. Least squares regressions on these data yield four
statistically significant natural cycles. The annual cycles are approximated by a triangle wave
form with one harmonic (cos(x)+cos(3x)/9). The three other cycles are sine waves with
lengths of 11.11, 39.05, and 79.01 years. The regression accounts for nearly 60% of the
variability.”

2. Sinusoidal regression, a long-known method, produces an amplitude and
phase for a presumed period that is only as valid as the presumption of a
spectral line holds at that period.

3. Its proper application to identify the tidal constituents in accordance
with Doodson’s method requires HOURLY data over an entire 18.361yr Metonic
cycle, not just monthly sea-level data obtained from the former.

4. Professional implementation of Doodson’s method for a site in an
resonant embayment such as S. F. is at least a few weeks’ work.

5. Proper spectrum analysis of Calais sea-level data reveals a bimodal
structure, with the bulk of variance in the low-frequency bands peaked at
~66yrs, as shown by results I posted numerically.

6. Confidence intervals for cross-correlation of time-invariant data
are grossly inadequate for rejecting any relationship between SSN and SST,
given the patently different spectral structures and locations of major
spectral peaks in the empirical time-series.

Now he refuses to examine his own calculations of ccf beyond a narrow range
of lags, while passing off cross-spectrum analysis (a well-established
methodology in signal and system analysis) as MY “whiz-bang method.” I
suspect that discarding unreliable SST data before the year (1905?) when
WMO set standards for observations by ships of opportunity might prove him
wrong even on HIS amateurish grounds of judgement.

Instead he turns a polemicists blind eye to all of the foregoing and
assumes the illusional mantle of authority on the question of SSN and SST
relationship, as if he has ever walked the walk on any substantive
scientific grounds.

As I said before, 1sky1 specializes in telling people that they are wrong … but he never, ever deigns to show us how to do it right. I invited him to do so above, as follows:

Actually, a good starting point to ending “analytic foolishness” would be your worked example using Nir Shaviv’s data. Nir Shaviv has provided his worked example. I’ve provided mine, using the same data.

And once again, you’re all mouth, nothing but theory and no results. You might be 100% correct, 1sky1, you seem like a smart guy. But claiming you are correct hardly demonstrates it. So far, you’ve talked the talk, but you’ve never walked the walk.

Here we have a clear example. Shaviv claims that there is a significant correlation between sunspots and SSTs. I say no. I’ve posted up the sunspot and the SST data.

So what does your whiz-bang method say, 1sky1? Are they significantly correlated or not? Mumbling about theory doesn’t cut it, my friend. It’s time for a worked example.

Or not, your choice. I’ve heard plenty of hilarious reasons from you as to why you never produce any actual results, but I doubt that you’ve run out of excuses yet …

Unfortunately, rather than show us that he can actually do it, and thus educate us in how to do it properly, 1sky1 does nothing but rage on and on about what a fool I am and how little I understand … perhaps true, but non-responsive.

1sky1, I may be a total fool. But I’ve done my analysis and archived my data and code. And Nir Shaviv has done his analysis and revealed his methods and data.

And until you do the same, until you show us that you can actually do it, I’ll continue to point and laugh at your vapid fulminations. As they used to say on the ranch I grew up on, “Pardner, you’re all hat and no cattle” …

A thermostat is a notch filter for all frequencies less than the response frequency of the system. (not exactly correct but the farther [lower frequency] from the response frequency the more correct)

So that leaves a question. Why 11 years? Why not 50 also? Or 500 in addition? You have not thought this through carefully. Very unlike you.

Sorry, but I’m not following you. If I have a house with a thermostat, what is the “response frequency” of that system? One year? One minute? I don’t understand what that means, and until I understand it I can’t answer it.

In any case, it sounds like you are claiming that the thermostat is a high-pass filter, not a notch filter. A notch filter is the inverse of a bandpass filter, it passes all frequencies above and below the band of the notch. A thermostat does nothing of the sort.

As to your accusation that I haven’t thought this through, I thought the same about you, but I was too polite to say so.

Once again, Willis fails to show the ccf for longer lags and more reliable
data intervals, all the while trying to turn the burden of proof around with
polemical ploys. The insistence that anyone challenging his inept
methodology is somehow obligated to spend time preparing a tutorial on suitable methods with worked examples is transparently self-serving.

In this post at least I have to agree that Willis comes across as the antithesis of a scientist and the very picture of an attention-seeking, grandstanding pathological projector of his own less pleasant traits onto others.

Why not pick one of the proffered papers mentioning discovery of a c. 11-year cycle to analyze, instead of commenting only upon those he felt he could counter? Just makes him look like a scaredy cat afraid of what he might find out.

sturgishooper said in part June 17, 2014 at 5:20 pm:
“….Why not pick one of the proffered papers mentioning discovery of a c. 11-year cycle to analyze”…,

It hard to tell from your comment who the “why not” challenge is directed at. In one interpretation you seem to be offering to pick the paper yourself, which is really what WOULD makes the most sense by far. Which SPECIFIC paper (or papers) do you suggest?

If possible provide the link to a downloadable copy (free please) or just give us your own on-point summary of your own reading of it.

“….Why not pick one of the proffered papers mentioning discovery of a c. 11-year cycle to analyze”…,

It hard to tell from your comment who the “why not” challenge is directed at. In one interpretation you seem to be offering to pick the paper yourself, which is really what WOULD makes the most sense by far. Which SPECIFIC paper (or papers) do you suggest?

If possible provide the link to a downloadable copy (free please) or just give us your own on-point summary of your own reading of it.

I have already analyzed a bunch of the “proferred paper”. At this point, as I said to Catherine, Sturgishooper, if you will pick one study that you think merits analysis, I’m glad to look at it … but you haven’t said which one you want me to look at. Let me know when you want to stop being a “scairdy-cat” (to use your charming term), and tell me which specific study you’d like me to analyze.

And as Bernie said, if you’d also give us a summary of what the paper says, that would be great.

Or you could just continue to sit on your … cushion and bitch and whine about what a jerk I am. It’s what 1sky1 does, refuses to do anything useful even when asked, and instead wastes his time whimpering about how I’m an idiot and boasting about how he’s a genius. You’re welcome to join him on that bench if you wish …

Once again, Willis fails to show the ccf for longer lags and more reliable
data intervals …

I got to thinking about this, and I realized it makes no sense at all. He wants me to show the cross-correlation out to 22 years.

So he is claiming that there is no significant effect in the present, and there is no significant effect with a lag of one year or a two years or a five years or eleven years, but despite that, he believes there is a significant warming effect which does not appear until twenty-two years after the sunspot peak.

Is there anyone here other than 1sky1 who actually believes that?

Yep, he’s a legend in his own mind, alright … but anyone who thinks that a 22-year lag in solar data is meaningful is a signal engineer who has never, ever taken a look outside his workplace.

I can see, however, why 1sky1 won’t give us a worked example … if I believed that kind of nonsense, a sea surface temperature effect that doesn’t occur until 22 years after the sunspot peak, I’d hide my light under a bushel too …

If the Climate is totally insensitive to TSI then the response line should be flat. No notch. The claim is that the notch shows up in Fourier Transform as well as the OFT.

Dave/Jo claim to have found a mechanism to explain the notch. We shall see.

Motl says, “But it must be either something else than the TSI, or the effect must be such that all the wiggles shorter than 20 years or so must be universally suppressed.”

And then goes on to discount “something else than the TSI” while also discounting the response at 3 years.

I’m seeing this a LOT among the sceptics of the theory.

There is a hole in their thinking because they are wedded to “TSI does not influence temps”. Amusing.

In any case it is amusing to see the “no response to small changes” orthodoxy. Compare with Dalton, Maunder, Oort, and other minimums.

====
BTW Dave/Jo think it is something other than TSI but that has the same clock. Of all the “experts” commenting Monckton is the only one who has seen the whole show (a preprint of the 170 page paper). I give him more credence because the rest are commenting based on incomplete data.

but anyone who thinks that a 22-year lag in solar data is meaningful is a signal engineer

Yep.

Now explain the response at 3 years.

First you’ll have to define “the response at 3 years”. If you mean the peak at 3 years in the famous “notch” graphic, I’ve asked Dave how he explains that, and I’m waiting for his answer. It’s his peak, and I’ve never said I had an explanation for it, so I’m not sure why you’re asking me and not David.

Let’s see … it’s a physics problem. Would I rather have a PhD physicist agree with me, or a British Lord? With all due respect to my friend Christopher Monckton, I’m going with the physicist.

If the Climate is totally insensitive to TSI then the response line should be flat. No notch. The claim is that the notch shows up in Fourier Transform as well as the OFT.

Say what? Clearly, you’re not following the story. If the climate is totally insensitive to TSI then there WILL be a notch. If that is your level of understanding, why are you even in this discussion?

In any case, I say that the climate is generally insensitive to small changes in TSI because it is regulated by emergent phenomena.

Dave/Jo say that the climate is generally insensitive to small changes in TSI because of a mechanism that to my knowledge has never been shown to exist—a thermal notch filter.

Dave/Jo claim to have found a mechanism to explain the notch. We shall see.

Despite my respect for Jo, this is a crappy way to put forwards a theory. In my opinion, it is a sales technique, and for me a very unpleasant one. Teasing us with claims of future genius? Sorry, not my style.

Motl says, “But it must be either something else than the TSI, or the effect must be such that all the wiggles shorter than 20 years or so must be universally suppressed.”

And then goes on to discount “something else than the TSI” while also discounting the response at 3 years.

I’m seeing this a LOT among the sceptics of the theory.

Sorry, but since I don’t do that, it’s of no interest to me. Go complain to Lubos or to the un-named “skeptics of the theory” you refer to above. One of the rules I try to follow is “Only give complaints or suggestions to someone who can do something about them.”

There is a hole in their thinking because they are wedded to “TSI does not influence temps”. Amusing.

Your amusement is indeed amusing. Do you truly not understand that David Evans is saying that TSI does not influence temps? He just has a different explanation for why that is than I do.

In any case it is amusing to see the “no response to small changes” orthodoxy. Compare with Dalton, Maunder, Oort, and other minimums.

Please let us all know how the temperature changed during the Dalton minimum. We actually have thermometers for that one, Berkeley Earth has the data. You’re just mouthing the solar party line. Go take a look at the actual data. Nowhere that I’ve seen is there any observational evidence of colder-than-usual temperatures during the Dalton minimum. Not in the Armagh Observatory data. Not in the CET observations. Not in the Berkeley Earth data.

And the Oort Minimum, which is claimed to be from 1040 to 1080 … are you kidding? Are you seriously claiming we have accurate enough data to determine if the world got colder, for forty years, a thousand years ago? You’re just repeating speculation as fact, nobody can show whether 1040-1080 was warmer or colder than the forty years on either side of that.

====
BTW Dave/Jo think it is something other than TSI but that has the same clock. Of all the “experts” commenting Monckton is the only one who has seen the whole show (a preprint of the 170 page paper). I give him more credence because the rest are commenting based on incomplete data.

Give credence to whoever you want. I give credence to my own analysis, in part because neither you nor David Evans nor anyone else have been able to find any fault with it.

Look, M., David started with a wrong idea. He proclaimed loudly and clearly to all and sundry that if there was a notch, there had to be a notch filter. Sorry, not true, and a very bad start.

He has since agreed with me that in any one of a number of systems, THE EXISTENCE OF A NOTCH DOES NOT MEAN THAT THERE IS A NOTCH FILTER. Said it himself, to his credit … but then he refused to grasp the nettle and think about what that means for his claims. You see, he has no evidence other than the notch to back up his theory … but the notch, as even he agrees now, doesn’t mean there is a notch filter, so that doesn’t back up his theory either.

Additionally, he has no other example of the type of mechanism he imagines, which is a thermal notch filter. I’ve never heard of one before, and I can’t even begin to imagine how one would work. Have you heard of such a strange beast? Where can I find one?

Finally, I find this all very bizarre. Over at Jo’s, David Evans is receiving kudos for saying that there is no 11-year cycle in the climate datasets … and meanwhile, here, I get insults and brickbats for saying exactly the same thing, that I can’t find any trace of the 11-year cycle in climate datasets.

We just differ as to why that is. I’ve provided a fully developed hypothesis and a host of observational data to support my explanation.

David Evans has provided no hypothesis, no observational data, and a “notch” that doesn’t mean what he thought it meant.

“David Evans has provided no hypothesis, no observational data, and a “notch” that doesn’t mean what he thought it meant.”

Have you read the whole paper? How do you know that such will not be provided? Monckton has read the whole paper. He seems satisfied. You are basing your argument on “I haven’t read the whole thing but it seems to me”. I have read the hints. They seem to make a point. But I’m a little schizophrenic and can see patterns where others don’t when the data is sparse. Of course some times it is imaginary fill in the blanks. So you have that going for you. But I have Monckton.

And Monckton told you specifically to be patient – that it will make sense in due course. So I’d be careful. The internet is forever. OTOH I should be careful too. My mates say I’m notorious for jumping the gun. Recklessly.

allows for more energetic particles to leave the sun.
These are caught by our atmosphere to form more ozone, (from oxygen), more peroroxides (from H-O) and more nitrogenous oxides (from nitrogen / oxygen)
or else we would die…..
In turn more ozone (as apparent from 1995), & more of the others, deflect more sunlight to space.
Hence we are cooling whilst the sun is actually brighter.
Let me know what you think of that?

“David Evans has provided no hypothesis, no observational data, and a “notch” that doesn’t mean what he thought it meant.”

Have you read the whole paper? How do you know that such will not be provided?

When he provides evidence, I’ll read it. When he provides a hypothesis as to how the notch filter works, I’ll consider it.

However, when a man puts out a theory without evidence or a physical mechanism, then he has to live with that. As I said before, to me that’s just a cheesy sales job. He obviously wants to get a bunch of people to sign on to his claims without bothering with evidence until they are already believers … and if you’re any example, he’s been successful.

Read the adulation that’s been heaped on him BEFORE he’s even explained his hypothesis or given a single example … is that your idea of how science should be conducted? What’s next, voting by twitter on how brilliant David Evans is, after which he’ll reveal his secrets?

Monckton has read the whole paper. He seems satisfied.

Christopher is my friend, and both a good guy and a brilliant man. However, I wouldn’t bet the farm on his public pronouncements.

You are basing your argument on “I haven’t read the whole thing but it seems to me”.

You just made that up out of the whole cloth.

My argument was with David’s claim that a notch means that there must be a notch filter. I based my argument on examples of systems which do have a notch, but which do NOT have a notch filter … and David agreed with me.

So I fear I have no clue about where you get the idea that I based my objection on “it seems to me”. David AGREED WITH MY ARGUMENT, M … so it must have been based on something pretty solid, wouldn’t you say?

I have read the hints. They seem to make a point. But I’m a little schizophrenic and can see patterns where others don’t when the data is sparse.

Mmmm … I don’t know if bringing up that you are a crazy guy who sees patterns at this point in the discussion is actually helping your scientific case here …

So lets see. There is a magnetic cycle that is anti-phase to the sun-spot cycle.

And then there was the hint about albedo. Maybe the magnetic field affects the albedo. Maybe through cosmic rays. That would provide signal cancellation (the notch). That would be why it takes two cycles for the signal to show up. And imperfect cancellation might very well explain the hump at three years.

As to being crazy – well as an engineer it has helped me solve problems a LOT faster than other engineers. After all I moved up from bench technician to aerospace engineer sans degree of any kind. Not too shabby. My method is to see a pattern and see if I can find the causes. In other words I get a clue and look for causes. In this case I’ll just have to wait. .

So far the people who get it are EEs. The rest of you are more or less in the dark. I find that highly amusing. But I get Dave’s methods. Standard DSP/electronics stuff. And the notch gave him a clue. But his wife (Jo) solved the problem. I’m sure the uproar has made them closer.

I got a mention. So I’m not just an EE either. And let me just say that money is not a problem. They are looking so the article is not telling a lie. Maybe I can tell you the whole story some time down the road. The whole AGW scam will be coming apart on many fronts. The Jo/Dave thing is just one of them.

And one other thing. I’m an old usenet hand. Hard arguments and insults don’t move me. So fire away. Hunter S. Thompson once told me on usenet that he liked my writing. It is a fun life. I’m enjoying the hell out of it.

Maybe later when this all calms down we can collaborate on some interesting stuff. I have some ideas.

I look forward to pulling the rug out from under those communists we used to know.

I agree there is no notch filter. It is just an electronic analog of what is happening. But I got that from the get go. I understood implicitly (and probably didn’t make clear the “implicit”) that Dave was making an electronic analog of what is happening. BTW introducing an anti-phase signal is how filtering is done in the analog domain. So the “not a filter” IS a filter. If you were an EE you’d know it in your bones.

I’m particularly enjoying my interactions with the “toob” guy. That is the era I grew up in. Love solid state but I have a certain nostalgia for valves as our Brit cousins call them.

I don’t think I’m going to answer any of your other points. Letting this unfold further is probably the best way. And if you see this before my previous – it got eaten by the system. It will show up as soon as someone gets to it.

sturgishooper said June 18, 2014 at 8:01 am June 17, 2014 at 6:22 pm:
“Sorry if confusing. I was replying to 1sky1 re. Willis, so thought it would be obvious that I was suggesting that he pick one of the proffered studies.”

Thanks – that is more clear.

It does seem to me that the very few papers actually listed here have been pretty well debunked (like tree rings from ONE TREE) and/or do not address the sea surface temperatures as specified.

What is needed however is not so much being specific about the few already listed (and “recycled”) but new lists which the commenter has actually read, analyzed, and gleaned. But you can’t really insist that someone be pinned down unless you have proper pins.

The link you gave at kidswincom was flashy but seemed to be a lightly annotated former slide show, and was very difficult to follow with no figures numbered or referenced, and a lot of undirected language. Must have been better with a presenter talking at length about a particular figure that was then simultaneously displayed right there on the screen. It wasn’t a proper paper (not that I care if it was peer reviewed or just peered at!). And then we see the author say “I have applied a trial and error least squares curve fitting technique”. Can’t be both trial-and-error and least-squares.

@m simon
the evidence of increasing ozone can be easily verified.
Ozone has been declining since the early 1950ies
now increasing since 1995.
Recovery in the SH has been much more spectacular than NH
meaning that less heat will go in the oceans.
do you understand the principle of absorption
and re-radiation?

I hope you come right there with actually getting that energy from nuclear fusion….
I have been known to take opposing views here [at WUWT] to those that think we need or even want energy from splitting atoms, for safety reasons.
Are you sure nuclear fusion is safe? Can you contain the process?

As far as I remember, during the course of my investigations, Trenberth is the only person to have noticed that ozone accounts for at least 20-25% of all energy being absorbed and re-radiated [50% back radiated to space] by the atmosphere. However, he never ever realised that same (increasing) energy of the UV-C * type that forms (more) ozone also makes (more) hydrogen peroxides and (more) nitrogenous oxides from its elements. Sadly, nobody is even monitoring the concentrations of these chemicals…..
[They call it now: Trenberth’s missing energy]
Namely [I suspect] that besides said ozone, the peroxides and other chemicals being formed also absorb and re-radiate energy back to space. So all together, a change in the concentration of all these chemicals might cause a slight change in energy coming into the oceans, mostly, the energy of the other UV types.
Hence, earth is cooling while the sun is “slumping” i.e. at its maximum brightness
(btw, am I the only person to have noticed that the sun is currenlty brighter? Cannot we simply measure that in Lux?)

*[my current understanding is that extreme UV, which is deadly to humans, is termed UV-C]

M Simon says
but if you turn off the switch on Polywell it shuts down.
Henry says
I like that. It sounds like switching off the gas (supply) if there is a fire.
But I don’t mind much using gas for energy because it supplies dung to the air for more greening and more crops. My latest update on my tables showed a perfect binomial curve for the decrease in minimum temperatures, 100 % correlation. No room for any “man-made” warming whatsoever.
All warming and all cooling is perfectly natural. Gas is good!!!

The baseless projections here are getting totally surreal. It is Willis who
puts words in my mouth by writing: “So he is claiming that there is no
significant effect in the present, and there is no significant effect with
a lag of one year or a two years or a five years or eleven years, but
despite that, he believes there is a significant warming effect which does
not appear until twenty-two years after the sunspot peak.”

Far from ever making any such claim, I was simply addressing the
behavior of the sample ccf at longer lags than those shown in Figures 2&3.
Such behavior shows much about the bandwidth of cross-correlated signal
components. Furthermore, in this particular case, we get values of 0.23,
0.27, 0.22 at lags of 0-2yrs and .21 and .19 at lags of 21-22yrs, when the
unreliable SST-index values prior to 1905 are disregarded. These are
statistically significant values by Willis’ OWN simplistic criterion!

But they do NOT necessarily indicate significant cross-spectral coherence,
which ensues from stable phase relationship. In turn, even strong coherence
is merely a necessary, but INSUFFICIENT condition for establishing a causal
connection. While it’s long been an open secret amongst professionals that
Hadley SST is far from scientifically dependable in such pursuits, the
cross-spectral phase shows that it DOES lag SSN by <1yr in the ~11yr
spectral band, albeit with only marginal coherence.

All the presumptions, projections and ad hominems constantly encountered
here can hardly hide incompetence. It's a waste of my time to keep
debunking at every turn the fundamental misconceptions of blog lions
who have scant clue of the meaning of the numbers they compute, yet fancy
themselves geophysical signal analysts.

… It’s a waste of my time to keep debunking at every turn the fundamental misconceptions of blog lions who have scant clue of the meaning of the numbers they compute, yet fancy themselves geophysical signal analysts.

And despite it being a waste of your precious time, once again here you are, comment after useless comment telling us of your unmatched brilliance and my rampant idiocy, but never actually doing anything. You give us nothing. No graphs, no analyses, no code, no datasets, no worked examples, no demonstration of how to do it right so that we might actually learn something … in fact, you provide nothing but endless unceasing claims that I’m wrong, wrong, wrong …

Sorry that your comment was overlooked in the short time I can allot for
blog activity.

Instead of delving into studies of proxy data etc. claiming a physical
connection with SSNs–which involve other issues–I have chosen to focus on
the purely METHODOLOGICAL mistakes painfully obvious in Willis’ inept
dismissal of the indications provided by the sample ccf with Hadley SST.
Having done a thorough analysis some years ago of the relationship between
sunspots and a more reliable marine temperature data set (not in the public
domain), I had a pretty good idea what to expect. That exhibited a
significantly different sample ccf than the highly inconsistent results he
shows in Figures 2&3. More to the point, it showed much stronger coherence
near ~11yr periods!

Indeed, Willis continues to evade showing his ccf for longer lags for fear
of being hung by his own petard. As I showed yesterday by posting some ccf
values for Hadley SSTs in the interval 1905-2013, they are significant even
by his criterion. Pathologically, he resorts to the trick of hiding that sample ccf behind this barrage: “You give us nothing. No graphs, no analyses, no code, no datasets, no worked examples, no demonstration of how to do it right so that we might actually learn something … “

1. Re-examining confidence intervals and their meaning.
2. Looking at the different effect that strong and weak solar cycles have on the data.

After replicating Shaviv’s results for the truncated analysis, 1955 – 2003, using folding. You concluded:

“If the 95%CI includes the zero line, it means the variation is not significant. The problem in Figure 6, of course, is the fact that there are only three cycles in the dataset. As a result, the 95%CI goes “from the floor to the ceiling”, as the saying goes, and the results are not significant in the slightest.”

I agree that having applied the “Gold Standard” criteria (95%CI), the results are not significant. However, given that good data are limited, and using nomenclature borrowed from the IPCC, you could say that, based on significance at one standard deviation (Shaviv):

“There is likely a correlation between SIDC Sunspot numbers and SST.”

You had another problem with the truncated analysis, 1955 – 2003:

“I’m sure you can see the problem. Because the dataset is so short (n = 49 years), there are only four solar minima—1964, 1976, 1986, and 1996. And since the truncated data ends in 2003, that means that we only have three complete solar cycles during the period.”

Again, I agree with this assessment. You go on to show the folded analysis for the entire dataset 1870 to 2013, and conclude:

“And despite having a much narrower 95%CI because we have more data, once again there is no statistically significant departure from zero.”

And again, I agree. However, the entire SIDC Sunspot dataset has many “weak” sunspot cycles, and it makes sense that what little signal we see in your figure 6 (largely spanning the modern maximum), is completely diluted and hidden by noise in your figure 7.

Your final conclusion:

“And so once again, I find that the claims of a connection between the sun and climate evaporate when they are examined closely.”

What if we take the three strongest cycles (peaking at 1955, 1978 and 1989 and fold them as you suggest:

“If I were doing it, I think I’d align them at the peak, and then take the averages for say six years on either side of the peak.”

Although the resulting signal in the folded SST averages is still not significant at the 95%CI, it is on the threshold of significance at the 90% confidence interval, allowing us to rephrase our conclusion using a stronger term (again borrowed from the IPCC) than before and adding a medium confidence because of our 3 cycle limit:

“There is very likely a correlation between SIDC Sunspot numbers and SST when the cycles are at their strongest – medium confidence.”

Take the three weakest peaks at 1883, 1893 and 1907, fold the data and the signal disappears completely within the noise, no significance at 1SD, adding weight to the suggestion that only the stronger cycles are worthy of analysis.

1. Re-examining confidence intervals and their meaning.
2. Looking at the different effect that strong and weak solar cycles have on the data.

….

Although the resulting signal in the folded SST averages is still not significant at the 95%CI, it is on the threshold of significance at the 90% confidence interval, allowing us to rephrase our conclusion using a stronger term (again borrowed from the IPCC) than before and adding a medium confidence because of our 3 cycle limit:

“There is very likely a correlation between SIDC Sunspot numbers and SST when the cycles are at their strongest – medium confidence.”

Bill, the IPCC has done science in general a huge disservice by their attempts to convince people that e.g. a 90% CI is worth more than a bucket of warm spit.

It isn’t.

And in particular, it is worthless in climate science, where apparently real cycles bounce into and out of existence like the Cheshire Cat. In science, there is no “likely” and “very likely”, that’s a joke.

Next, you say that

However, the entire SIDC Sunspot dataset has many “weak” sunspot cycles, and it makes sense that what little signal we see in your figure 6 (largely spanning the modern maximum), is completely diluted and hidden by noise in your figure 7.

That makes no sense at all. Why should the fact that some cycles are stronger than others mean that the signal would be “completely diluted and hidden by noise’?

Finally, when a signal is so “completely diluted and hidden by noise” that it is lost in the weeds … well, I call that no significant relationship between sunspots and sea surface temperature.

I know full well why Willis would like to end this debate. See my comment above.

End this debate? Once again you make an inane claim about what I’ve said without quoting my words. Typical, but understandable, since I’ve said nothing of the sort.

In fact, 1sky1, far from wanting to end this debate, I’m the guy who started this debate about sunspots several posts ago, and I’m more than happy that it’s gone on this long and glad for it to continue.

What I would like, however, is for you to join the debate by actually giving us a worked example, or some data and code, or showing us your results… well, anything but your endless litany about how stupid I am and how smart you are.

You seem to think that repeated claims that I’m wrong somehow make you right. The only way for you to be right is to PRODUCE SOME WORK. So far all you’ve showed us is your big mouth.

Nir Shaviv has shown his work and his results. I’ve shown my work and my results.

And you? You’ves shown nothing but endless uncited, unquoted, unreferenced, and unsupported claims of how much smarter you are than all the rest of us, and how well you understand signal analysis, and oh, yeah, how cataclysmically wrong, ignorant, and stupdi I am.

If you would like to join the debate, 1sky1, rather than continuing to make a fool of yourself by standing on the sidelines and throwing spitballs, please show your work and your results.

It’s called “science”, and it exists only with transparency of data and methods. You should try it some time.

The marine data set I referred to may some day become public, but it is not
in my purvue alone to make that happen. I can vouch, however, that if its
analysis ever should go public, then the venue won’t be a blog.

What’s intriguing is the supposition that changing the mind of a blog-writer
with no scientific qualifications ultimately matters. Over the last few
threads I endeavored to steer Willis away from fundamental misconceptions
in geophysical signal analysis, providing references both on-line and in
print, even working an example of the proper power spectrum of the Cascais
sea-level data. It was received with the sound of crickets, broken only by
a barrage of ad hominems. He continued unabashed to other amateurish
misconceptions, far more egregious than those of some inept analysts I’ve
fired in the past. And now, ignoring all my pointers, he continues to hide
the ccf values at long langs, while preaching about how science should
done.

There may be an audience of scientific novices out there in the ether who
can swallow that, what with pretty graphs and smooth sales patter. I’m
certainly not part of it and have none of the ambitions of a blog lion. There
are far bigger fish in government and academia to fry.

farmerbraun says
40.3550° S, 175.6117° E
henry says
if that is your position, then you are probably a retired farmer?
Nevertheless, for the other farmers,
at the higher latitudes >[40] it will become progressively drier, from now onward, ultimately culminating in a big drought period similar to the dust bowl drought 1932-1939. My various calculations all bring me to believe that this main drought period on the Great Plains will be from 2021-2028. It looks like we have only 7 “fat” years left…..

@farmerbraun
sorry, I see now that you live in New Zealand.
Note that I did a study on rainfall in Wellington
I found that between 1930-1940 rainfall in Wellington was 15% lower than the average 1940-2014.
Hence, you can expect the same between 2015-2025

Yes , it does make a great deal of difference that I live on a small island in a vast ocean.
So you will be aware that this will likely play out very differently in N.Z.
In all my reading on future climate so far for this region, I have not seen any significant advance on what we were told back in 1999 by Augie Auer ; namely that, for the following 30 or so years, we should expect a predominance of la Nina over el Nino, and that cool wet summers could be expected in about eight years out of ten.
This was in contrast to the period from ca 1975-1998 when el Nino was expected to predominate , and of course we had already experienced mostly hot dry summers throughout that period , to the extent that many thought that this was the norm. I can only recall a couple of slightly wet summers in that entire period.

So the change has been profound on a non-irrigated, pastoral farm on shallow recent soils (river-bed). I have to say that i like it ; the farm is more productive, and feed costs are reduced due to the lengthier period of pasture growth.
The dry summer during la Nina is tough because it is dry and relatively cool, but compared to the lengthy hot dry periods of the previous PDO phase , it is still a breeze to farm with.

The most that one can wish for , is to still be alive when the PDO switches back : it should be fun.

Whenever I get a result in climate science with an rquare of 0.9999, I know I’ve screwed up somewhere. Literally. When I get that kind of result, I get very nervous. Real-world correlations are never that good, it means that I’ve made some kind of error somewhere along the line. And whether I can find the error immediately or not, I wouldn’t dream of publishing such results. Even if I couldn’t find the error, I would never trust it. Like they say, if it sounds too good to be true … it likely is.

Thanks, Willis, for your reply.
You commented on my borrowing terms from the IPCC:

“Bill, the IPCC has done science in general a huge disservice by their attempts to convince people that e.g. a 90% CI is worth more than a
bucket of warm spit.

It isn’t.”

I acknowledge your comment about the IPCC and it’s (mis) use of these terms, and accept your (implied) rejection of any CI less than 95%.
But nevertheless, I saw a “signal” in your Figure 6, and further, thought I had data that indicated that the strength of the signal in the SST was dependent on the strength of the solar cycle.

Rather than, as you put it: “apparently real cycles bounce into and out of existence like the Cheshire Cat”

OK, perhaps I didn’t articulate that point very well, and you said:
“That makes no sense at all. Why should the fact that some cycles are stronger than others mean that the signal would be “completely diluted and hidden by noise’?”

Let me try again. (I will leave out the data this time because they format badly.)

I took the three cycles with highest SIDC Sunspot numbers (1957, 1979, 1989), folded the detrended SST data 7 years either side of the peaks and looked at the plot of the averages.
The plot looks sinusoidal, with a gentle trough followed by a gentle peak.
Peak to peak signal estimated at +/-0.085 degrees celcius.
The signal is not significant at the 95%CI.

I took three mid-strength cycles (1917, 1968, 2000), folded the detrended SST data 7 years either side of the peaks as before and looked at the plot of the averages.
The plot looks less sinusoidal. There is a sharp peak mid trough, but the following gentle peak is still noticeable. (I interpret this as partial corruption by noise)
Peak to peak signal estimated at +/-0.05 degrees celcius.

I took the three cycles with lowest SIDC Sunspot numbers (1883, 1893, 1905), folded the detrended SST data 7 years either side of the peaks and looked at the plot of the averages.
The plot looks nothing like sinusoidal. The signal is lost in the noise.
A peak to peak estimate appears to be meaningless. Assumed to be 0.

I draw two conclusions from this:
1. From the analysis of the highest SIDC Sunspot number cycles only:
High strength solar cycles may have an effect on SSTs but is not significant at the 95%CI.
2. From the entire analysis of three high, mid and low SIDC Sunspot number cycles:
The strength of the solar cycle may have an effect on the strength of the SST signal.

On its own merits, the first conclusion is not worthy of consideration, but is corroborated well by the second conclusion.

And when you say:
“Finally, when a signal is so “completely diluted and hidden by noise” that it is lost in the weeds … well, I call that no significant relationship between sunspots and sea surface temperature.”

This is only true for the SST signal during sunspot cycles with low counts.

I would instead conclude, Willis, that:
There is evidence suggesting that strong solar cycles have an effect on SST, corroborated by further evidence that the strength of the solar cycle influences the SST signal strength.

I close by noting that Nir Shaviv complained in the comments above that I hadn’t analyzed the sea level data. I said I’d be glad to analyze it, and I asked him for the names of the 24 stations that he had used. He’s never gotten back to me with the list of the stations, so I fear that he may not be all that interested in my analysis … a reluctance which I can certainly understand, given the problems I found in the part of his study that I investigated above.